Differential Diagnosis Among Alzheimer's Disease, Mild Cognitive Impairment, and Normal Subjects Using Resting-State fMRI Data Extracted from Multi Subject Dictionary Learning Atlas
-
Farzad Alizadeh
,
Hassan Homayoun
,
Amir hossein Batouli
,
Maryam Noroozian
,
Forough Sodaie
,
Hanieh Mobarak Salary
,
Anahita Fathi Kazerooni
,
Hamidreza Saligheh Rad
Abstract
Purpose: A powerful imaging method for evaluating brain patches is resting-state functional magnetic resonance imaging, in which the subject is at rest. Artificial neural networks are one of the several Alzheimer's disease analysis and diagnosis methods which is used in this study. We investigate artificial neural networks' ability to diagnose Alzheimer's disease using resting-state functional magnetic resonance imaging data.
Material and Methods: Functional and structural magnetic resonance imaging data acquisition was applied for 15 Alzheimer’s disease, 17 mild cognitive impaired, and ten normal healthy participants. Time series of blood oxygen level dependent extracted from the multi subject dictionary learning brain atlas after pre-processing. This study develops a one-dimensional convolutional neural network using extracted signals of the functional atlas for differential diagnosis of Alzheimer's disease.
Results: Applying the proposed method to resting-state functional magnetic resonance imaging signals for classifying three classes of Alzheimer's disease patients resulted in overall accuracy, F1-score, and precision of 0.685 ,0.663, and 0.681 respectively. Using 39 regions in the brain and proposing a quiet simple network than most of the available deep learning-based methods are the main advantages of this model.
Conclusion: Resting-state functional magnetic resonance imaging signal recognition based on a functional atlas with the application of a deep neural network has a pattern recognition capability that can make a differential diagnosis with an acceptable level of accuracy and precision. Therefore, deep neural networks can be considered as a tool for the early diagnosis of Alzheimer’s disease.
1- Eric McDade and Randall J Bateman, "Stop Alzheimer’s before it starts." Nature News, Vol. 547 (No. 7662), p. 153, (2017).
2- Keith A Johnson et al., "Appropriate use criteria for amyloid PET: a report of the Amyloid Imaging Task Force, the Society of Nuclear Medicine and Molecular Imaging, and the Alzheimer’s Association." Journal of Nuclear Medicine, Vol. 54 (No. 3), pp. 476-90, (2013).
3- Marjolein MA Engels, Cornelis J Stam, Wiesje M van der Flier, Philip Scheltens, Hanneke de Waal, and Elisabeth CW van Straaten, "Declining functional connectivity and changing hub locations in Alzheimer’s disease: an EEG study." BMC neurology, Vol. 15 (No. 1), pp. 1-8, (2015).
4- Varvara Valotassiou, George Angelidis, Dimitrios Psimadas, Ioannis Tsougos, and Panagiotis Georgoulias, "In the era of FDG PET, is it time for brain perfusion SPECT to gain a place in Alzheimer’s disease imaging biomarkers?" European Journal of Nuclear Medicine and Molecular Imaging, Vol. 48 (No. 4), pp. 969-71, (2021).
5- Cathleen Haense, Karl Herholz, WJ Jagust, and Wolf-Dieter Heiss, "Performance of FDG PET for detection of Alzheimer’s disease in two independent multicentre samples (NEST-DD and ADNI)." Dementia and geriatric cognitive disorders, Vol. 28 (No. 3), pp. 259-66, (2009).
6- David López-Sanz, Noelia Serrano, and Fernando Maestú, "The role of magnetoencephalography in the early stages of Alzheimer’s disease." Frontiers in neuroscience, Vol. 12p. 572, (2018).
7- Samaneh Kazemifar et al., "Spontaneous low frequency BOLD signal variations from resting-state fMRI are decreased in Alzheimer disease." PloS one, Vol. 12 (No. 6), p. e0178529, (2017).
8- Seyed Hani Hojjati, Ata Ebrahimzadeh, and Abbas Babajani-Feremi, "Identification of the early stage of Alzheimer's disease using structural MRI and resting-state fMRI." Frontiers in neurology, Vol. 10p. 904, (2019).
9- Rik Ossenkoppele et al., "Associations between tau, Aβ, and cortical thickness with cognition in Alzheimer disease." Neurology, Vol. 92 (No. 6), pp. e601-e12, (2019).
10- Axel Montagne, Daniel A Nation, Judy Pa, Melanie D Sweeney, Arthur W Toga, and Berislav V Zlokovic, "Brain imaging of neurovascular dysfunction in Alzheimer’s disease." Acta neuropathologica, Vol. 131 (No. 5), pp. 687-707, (2016).
11- Ying Han et al., "Frequency-dependent changes in the amplitude of low-frequency fluctuations in amnestic mild cognitive impairment: a resting-state fMRI study." Neuroimage, Vol. 55 (No. 1), pp. 287-95, (2011).
12- Yongxia Zhou, Fang Yu, Timothy Q Duong, and Alzheimer's Disease Neuroimaging Initiative, "White matter lesion load is associated with resting state functional MRI activity and amyloid PET but not FDG in mild cognitive impairment and early Alzheimer's disease patients." Journal of Magnetic Resonance Imaging, Vol. 41 (No. 1), pp. 102-09, (2015).
13- Yong Liu et al., "Regional homogeneity, functional connectivity and imaging markers of Alzheimer's disease: a review of resting-state fMRI studies." Neuropsychologia, Vol. 46 (No. 6), pp. 1648-56, (2008).
14- Timo Tuovinen et al., "The effect of gray matter ICA and coefficient of variation mapping of BOLD data on the detection of functional connectivity changes in Alzheimer’s disease and bvFTD." Frontiers in human neuroscience, Vol. 10p. 680, (2017).
15- Angela Tam et al., "Common effects of amnestic mild cognitive impairment on resting-state connectivity across four independent studies." Frontiers in aging neuroscience, Vol. 7p. 242, (2015).
16- Benoît Magnin et al., "Support vector machine-based classification of Alzheimer’s disease from whole-brain anatomical MRI." Neuroradiology, Vol. 51 (No. 2), pp. 73-83, (2009).
17- Ian Goodfellow, Yoshua Bengio, and Aaron Courville, Deep learning. MIT press, (2016).
18- Mufti Mahmud, M Shamim Kaiser, T Martin McGinnity, and Amir Hussain, "Deep learning in mining biological data." Cognitive Computation, Vol. 13 (No. 1), pp. 1-33, (2021).
19- Janani Venugopalan, Li Tong, Hamid Reza Hassanzadeh, and May D Wang, "Multimodal deep learning models for early detection of Alzheimer’s disease stage." Scientific Reports, Vol. 11 (No. 1), pp. 1-13, (2021).
20- Olivier Colliot et al., "Discrimination between Alzheimer disease, mild cognitive impairment, and normal aging by using automated segmentation of the hippocampus." Radiology, Vol. 248 (No. 1), pp. 194-201, (2008).
21- Geraldo F Busatto et al., "A voxel-based morphometry study of temporal lobe gray matter reductions in Alzheimer’s disease." Neurobiology of aging, Vol. 24 (No. 2), pp. 221-31, (2003).
22- Mamoon Rashid, Harjeet Singh, and Vishal Goyal, "The use of machine learning and deep learning algorithms in functional magnetic resonance imaging—A systematic review." Expert Systems, Vol. 37 (No. 6), p. e12644, (2020).
23- Ali Nawaz et al., "A Comprehensive Literature Review of Application of Artificial Intelligence in Functional Magnetic Resonance Imaging for Disease Diagnosis." Applied Artificial Intelligence, pp. 1-19, (2021).
24- Longhai Li Wutao Yin, Fang-Xiang Wu,, "Deep learning for brain disorder diagnosis based on fMRI images." Neurocomputing, Vol. 469pp. 332-45, (2022).
25- Nguyen Thanh Duc, Seungjun Ryu, Muhammad Naveed Iqbal Qureshi, Min Choi, Kun Ho Lee, and Boreom Lee, "3D-deep learning based automatic diagnosis of Alzheimer’s disease with joint MMSE prediction using resting-state fMRI." Neuroinformatics, Vol. 18 (No. 1), pp. 71-86, (2020).
26- Mark Jenkinson, Christian F Beckmann, Timothy EJ Behrens, Mark W Woolrich, and Stephen M Smith, "Fsl." Neuroimage, Vol. 62 (No. 2), pp. 782-90, (2012).
27- Koene RA Van Dijk, Mert R Sabuncu, and Randy L Buckner, "The influence of head motion on intrinsic functional connectivity MRI." Neuroimage, Vol. 59 (No. 1), pp. 431-38, (2012).
28- Gaël Varoquaux, Alexandre Gramfort, Fabian Pedregosa, Vincent Michel, and Bertrand Thirion, "Multi-subject dictionary learning to segment an atlas of brain spontaneous activity." in Biennial International Conference on information processing in medical imaging, (2011): Springer, pp. 562-73.
29- Hassan Khastavaneh and Hossein Ebrahimpour-Komleh, "Representation Learning Techniques: An Overview." in The 7th International Conference on Contemporary Issues in Data Science, (2019): Springer, pp. 89-104.
30- Li Deng and Dong Yu, "Deep learning: methods and applications." Foundations and trends in signal processing, Vol. 7 (No. 3–4), pp. 197-387, (2014).
31- Rikiya Yamashita, Mizuho Nishio, Richard Kinh Gian Do, and Kaori Togashi, "Convolutional neural networks: an overview and application in radiology." Insights into imaging, Vol. 9 (No. 4), pp. 611-29, (2018).
32- Min Chen, Xiaobo Shi, Yin Zhang, Di Wu, and Mohsen Guizani, "Deep features learning for medical image analysis with convolutional autoencoder neural network." IEEE Transactions on Big Data, (2017).
33- Manjit Kaur and Dilbag Singh, "Fusion of medical images using deep belief networks." Cluster Computing, Vol. 23 (No. 2), pp. 1439-53, (2020).
34- Yechong Huang, Jiahang Xu, Yuncheng Zhou, Tong Tong, Xiahai Zhuang, and Alzheimer’s Disease Neuroimaging Initiative, "Diagnosis of Alzheimer’s disease via multi-modality 3D convolutional neural network." Frontiers in neuroscience, Vol. 13p. 509, (2019).
35- Hassan Homayoun and Hossein Ebrahimpour-Komleh, "Automated segmentation of abnormal tissues in medical images." Journal of Biomedical Physics & Engineering, Vol. 11 (No. 4), p. 415, (2021).
36- Haibing Guo and Yongjin Zhang, "Resting state fMRI and improved deep learning algorithm for earlier detection of Alzheimer’s disease." IEEE Access, Vol. 8pp. 115383-92, (2020).
37- Saman Sarraf and Ghassem Tofighi, "Deep learning-based pipeline to recognize Alzheimer's disease using fMRI data." in 2016 future technologies conference (FTC), (2016): IEEE, pp. 816-20.
38- Farheen Ramzan et al., "A deep learning approach for automated diagnosis and multi-class classification of Alzheimer’s disease stages using resting-state fMRI and residual neural networks." Journal of medical systems, Vol. 44 (No. 2), pp. 1-16, (2020).
39- Yosra Kazemi and Sheridan Houghten, "A deep learning pipeline to classify different stages of Alzheimer's disease from fMRI data." in 2018 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), (2018): IEEE, pp. 1-8.
40- Ciprian D Billones, Olivia Jan Louville D Demetria, David Earl D Hostallero, and Prospero C Naval, "DemNet: a convolutional neural network for the detection of Alzheimer's disease and mild cognitive impairment." in 2016 IEEE region 10 conference (TENCON), (2016): IEEE, pp. 3724-27.
41- Saman Sarraf, Danielle D Desouza, John AE Anderson, and Cristina Saverino, "MCADNNet: Recognizing stages of cognitive impairment through efficient convolutional fMRI and MRI neural network topology models." IEEE Access, Vol. 7pp. 155584-600, (2019).
42- Daniel Stamate et al., "Applying deep learning to predicting dementia and mild cognitive impairment." in IFIP International Conference on Artificial Intelligence Applications and Innovations, (2020): Springer, pp. 308-19.
43- Ali Khazaee, Ata Ebrahimzadeh, and Abbas Babajani-Feremi, "Application of advanced machine learning methods on resting-state fMRI network for identification of mild cognitive impairment and Alzheimer’s disease." Brain imaging and behavior, Vol. 10 (No. 3), pp. 799-817, (2016).
44- Keiichi Onoda et al., "Can a resting-state functional connectivity index identify patients with Alzheimer's disease and mild cognitive impairment across multiple sites?" Brain connectivity, Vol. 7 (No. 7), pp. 391-400, (2017).
45- Adrien Payan and Giovanni Montana, "Predicting Alzheimer's disease: a neuroimaging study with 3D convolutional neural networks." arXiv preprint arXiv:1502.02506, (2015).
46- Ehsan Hosseini-Asl, Georgy Gimel'farb, and Ayman El-Baz, "Alzheimer's disease diagnostics by a deeply supervised adaptable 3D convolutional network." arXiv preprint arXiv:1607.00556, (2016).
47- Ashish Gupta, Murat Ayhan, and Anthony Maida, "Natural image bases to represent neuroimaging data." in International conference on machine learning, (2013): PMLR, pp. 987-94.
48- Heung-Il Suk, Chong-Yaw Wee, Seong-Whan Lee, and Dinggang Shen, "State-space model with deep learning for functional dynamics estimation in resting-state fMRI." Neuroimage, Vol. 129pp. 292-307, (2016).
49- Chenhui Hu, Ronghui Ju, Yusong Shen, Pan Zhou, and Quanzheng Li, "Clinical decision support for Alzheimer's disease based on deep learning and brain network." in 2016 IEEE International Conference on Communications (ICC), (2016): IEEE, pp. 1-6.
2- Keith A Johnson et al., "Appropriate use criteria for amyloid PET: a report of the Amyloid Imaging Task Force, the Society of Nuclear Medicine and Molecular Imaging, and the Alzheimer’s Association." Journal of Nuclear Medicine, Vol. 54 (No. 3), pp. 476-90, (2013).
3- Marjolein MA Engels, Cornelis J Stam, Wiesje M van der Flier, Philip Scheltens, Hanneke de Waal, and Elisabeth CW van Straaten, "Declining functional connectivity and changing hub locations in Alzheimer’s disease: an EEG study." BMC neurology, Vol. 15 (No. 1), pp. 1-8, (2015).
4- Varvara Valotassiou, George Angelidis, Dimitrios Psimadas, Ioannis Tsougos, and Panagiotis Georgoulias, "In the era of FDG PET, is it time for brain perfusion SPECT to gain a place in Alzheimer’s disease imaging biomarkers?" European Journal of Nuclear Medicine and Molecular Imaging, Vol. 48 (No. 4), pp. 969-71, (2021).
5- Cathleen Haense, Karl Herholz, WJ Jagust, and Wolf-Dieter Heiss, "Performance of FDG PET for detection of Alzheimer’s disease in two independent multicentre samples (NEST-DD and ADNI)." Dementia and geriatric cognitive disorders, Vol. 28 (No. 3), pp. 259-66, (2009).
6- David López-Sanz, Noelia Serrano, and Fernando Maestú, "The role of magnetoencephalography in the early stages of Alzheimer’s disease." Frontiers in neuroscience, Vol. 12p. 572, (2018).
7- Samaneh Kazemifar et al., "Spontaneous low frequency BOLD signal variations from resting-state fMRI are decreased in Alzheimer disease." PloS one, Vol. 12 (No. 6), p. e0178529, (2017).
8- Seyed Hani Hojjati, Ata Ebrahimzadeh, and Abbas Babajani-Feremi, "Identification of the early stage of Alzheimer's disease using structural MRI and resting-state fMRI." Frontiers in neurology, Vol. 10p. 904, (2019).
9- Rik Ossenkoppele et al., "Associations between tau, Aβ, and cortical thickness with cognition in Alzheimer disease." Neurology, Vol. 92 (No. 6), pp. e601-e12, (2019).
10- Axel Montagne, Daniel A Nation, Judy Pa, Melanie D Sweeney, Arthur W Toga, and Berislav V Zlokovic, "Brain imaging of neurovascular dysfunction in Alzheimer’s disease." Acta neuropathologica, Vol. 131 (No. 5), pp. 687-707, (2016).
11- Ying Han et al., "Frequency-dependent changes in the amplitude of low-frequency fluctuations in amnestic mild cognitive impairment: a resting-state fMRI study." Neuroimage, Vol. 55 (No. 1), pp. 287-95, (2011).
12- Yongxia Zhou, Fang Yu, Timothy Q Duong, and Alzheimer's Disease Neuroimaging Initiative, "White matter lesion load is associated with resting state functional MRI activity and amyloid PET but not FDG in mild cognitive impairment and early Alzheimer's disease patients." Journal of Magnetic Resonance Imaging, Vol. 41 (No. 1), pp. 102-09, (2015).
13- Yong Liu et al., "Regional homogeneity, functional connectivity and imaging markers of Alzheimer's disease: a review of resting-state fMRI studies." Neuropsychologia, Vol. 46 (No. 6), pp. 1648-56, (2008).
14- Timo Tuovinen et al., "The effect of gray matter ICA and coefficient of variation mapping of BOLD data on the detection of functional connectivity changes in Alzheimer’s disease and bvFTD." Frontiers in human neuroscience, Vol. 10p. 680, (2017).
15- Angela Tam et al., "Common effects of amnestic mild cognitive impairment on resting-state connectivity across four independent studies." Frontiers in aging neuroscience, Vol. 7p. 242, (2015).
16- Benoît Magnin et al., "Support vector machine-based classification of Alzheimer’s disease from whole-brain anatomical MRI." Neuroradiology, Vol. 51 (No. 2), pp. 73-83, (2009).
17- Ian Goodfellow, Yoshua Bengio, and Aaron Courville, Deep learning. MIT press, (2016).
18- Mufti Mahmud, M Shamim Kaiser, T Martin McGinnity, and Amir Hussain, "Deep learning in mining biological data." Cognitive Computation, Vol. 13 (No. 1), pp. 1-33, (2021).
19- Janani Venugopalan, Li Tong, Hamid Reza Hassanzadeh, and May D Wang, "Multimodal deep learning models for early detection of Alzheimer’s disease stage." Scientific Reports, Vol. 11 (No. 1), pp. 1-13, (2021).
20- Olivier Colliot et al., "Discrimination between Alzheimer disease, mild cognitive impairment, and normal aging by using automated segmentation of the hippocampus." Radiology, Vol. 248 (No. 1), pp. 194-201, (2008).
21- Geraldo F Busatto et al., "A voxel-based morphometry study of temporal lobe gray matter reductions in Alzheimer’s disease." Neurobiology of aging, Vol. 24 (No. 2), pp. 221-31, (2003).
22- Mamoon Rashid, Harjeet Singh, and Vishal Goyal, "The use of machine learning and deep learning algorithms in functional magnetic resonance imaging—A systematic review." Expert Systems, Vol. 37 (No. 6), p. e12644, (2020).
23- Ali Nawaz et al., "A Comprehensive Literature Review of Application of Artificial Intelligence in Functional Magnetic Resonance Imaging for Disease Diagnosis." Applied Artificial Intelligence, pp. 1-19, (2021).
24- Longhai Li Wutao Yin, Fang-Xiang Wu,, "Deep learning for brain disorder diagnosis based on fMRI images." Neurocomputing, Vol. 469pp. 332-45, (2022).
25- Nguyen Thanh Duc, Seungjun Ryu, Muhammad Naveed Iqbal Qureshi, Min Choi, Kun Ho Lee, and Boreom Lee, "3D-deep learning based automatic diagnosis of Alzheimer’s disease with joint MMSE prediction using resting-state fMRI." Neuroinformatics, Vol. 18 (No. 1), pp. 71-86, (2020).
26- Mark Jenkinson, Christian F Beckmann, Timothy EJ Behrens, Mark W Woolrich, and Stephen M Smith, "Fsl." Neuroimage, Vol. 62 (No. 2), pp. 782-90, (2012).
27- Koene RA Van Dijk, Mert R Sabuncu, and Randy L Buckner, "The influence of head motion on intrinsic functional connectivity MRI." Neuroimage, Vol. 59 (No. 1), pp. 431-38, (2012).
28- Gaël Varoquaux, Alexandre Gramfort, Fabian Pedregosa, Vincent Michel, and Bertrand Thirion, "Multi-subject dictionary learning to segment an atlas of brain spontaneous activity." in Biennial International Conference on information processing in medical imaging, (2011): Springer, pp. 562-73.
29- Hassan Khastavaneh and Hossein Ebrahimpour-Komleh, "Representation Learning Techniques: An Overview." in The 7th International Conference on Contemporary Issues in Data Science, (2019): Springer, pp. 89-104.
30- Li Deng and Dong Yu, "Deep learning: methods and applications." Foundations and trends in signal processing, Vol. 7 (No. 3–4), pp. 197-387, (2014).
31- Rikiya Yamashita, Mizuho Nishio, Richard Kinh Gian Do, and Kaori Togashi, "Convolutional neural networks: an overview and application in radiology." Insights into imaging, Vol. 9 (No. 4), pp. 611-29, (2018).
32- Min Chen, Xiaobo Shi, Yin Zhang, Di Wu, and Mohsen Guizani, "Deep features learning for medical image analysis with convolutional autoencoder neural network." IEEE Transactions on Big Data, (2017).
33- Manjit Kaur and Dilbag Singh, "Fusion of medical images using deep belief networks." Cluster Computing, Vol. 23 (No. 2), pp. 1439-53, (2020).
34- Yechong Huang, Jiahang Xu, Yuncheng Zhou, Tong Tong, Xiahai Zhuang, and Alzheimer’s Disease Neuroimaging Initiative, "Diagnosis of Alzheimer’s disease via multi-modality 3D convolutional neural network." Frontiers in neuroscience, Vol. 13p. 509, (2019).
35- Hassan Homayoun and Hossein Ebrahimpour-Komleh, "Automated segmentation of abnormal tissues in medical images." Journal of Biomedical Physics & Engineering, Vol. 11 (No. 4), p. 415, (2021).
36- Haibing Guo and Yongjin Zhang, "Resting state fMRI and improved deep learning algorithm for earlier detection of Alzheimer’s disease." IEEE Access, Vol. 8pp. 115383-92, (2020).
37- Saman Sarraf and Ghassem Tofighi, "Deep learning-based pipeline to recognize Alzheimer's disease using fMRI data." in 2016 future technologies conference (FTC), (2016): IEEE, pp. 816-20.
38- Farheen Ramzan et al., "A deep learning approach for automated diagnosis and multi-class classification of Alzheimer’s disease stages using resting-state fMRI and residual neural networks." Journal of medical systems, Vol. 44 (No. 2), pp. 1-16, (2020).
39- Yosra Kazemi and Sheridan Houghten, "A deep learning pipeline to classify different stages of Alzheimer's disease from fMRI data." in 2018 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), (2018): IEEE, pp. 1-8.
40- Ciprian D Billones, Olivia Jan Louville D Demetria, David Earl D Hostallero, and Prospero C Naval, "DemNet: a convolutional neural network for the detection of Alzheimer's disease and mild cognitive impairment." in 2016 IEEE region 10 conference (TENCON), (2016): IEEE, pp. 3724-27.
41- Saman Sarraf, Danielle D Desouza, John AE Anderson, and Cristina Saverino, "MCADNNet: Recognizing stages of cognitive impairment through efficient convolutional fMRI and MRI neural network topology models." IEEE Access, Vol. 7pp. 155584-600, (2019).
42- Daniel Stamate et al., "Applying deep learning to predicting dementia and mild cognitive impairment." in IFIP International Conference on Artificial Intelligence Applications and Innovations, (2020): Springer, pp. 308-19.
43- Ali Khazaee, Ata Ebrahimzadeh, and Abbas Babajani-Feremi, "Application of advanced machine learning methods on resting-state fMRI network for identification of mild cognitive impairment and Alzheimer’s disease." Brain imaging and behavior, Vol. 10 (No. 3), pp. 799-817, (2016).
44- Keiichi Onoda et al., "Can a resting-state functional connectivity index identify patients with Alzheimer's disease and mild cognitive impairment across multiple sites?" Brain connectivity, Vol. 7 (No. 7), pp. 391-400, (2017).
45- Adrien Payan and Giovanni Montana, "Predicting Alzheimer's disease: a neuroimaging study with 3D convolutional neural networks." arXiv preprint arXiv:1502.02506, (2015).
46- Ehsan Hosseini-Asl, Georgy Gimel'farb, and Ayman El-Baz, "Alzheimer's disease diagnostics by a deeply supervised adaptable 3D convolutional network." arXiv preprint arXiv:1607.00556, (2016).
47- Ashish Gupta, Murat Ayhan, and Anthony Maida, "Natural image bases to represent neuroimaging data." in International conference on machine learning, (2013): PMLR, pp. 987-94.
48- Heung-Il Suk, Chong-Yaw Wee, Seong-Whan Lee, and Dinggang Shen, "State-space model with deep learning for functional dynamics estimation in resting-state fMRI." Neuroimage, Vol. 129pp. 292-307, (2016).
49- Chenhui Hu, Ronghui Ju, Yusong Shen, Pan Zhou, and Quanzheng Li, "Clinical decision support for Alzheimer's disease based on deep learning and brain network." in 2016 IEEE International Conference on Communications (ICC), (2016): IEEE, pp. 1-6.
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| Issue | Vol 9 No 4 (2022) | |
| Section | Original Article(s) | |
| DOI | https://doi.org/10.18502/fbt.v9i4.10423 | |
| Keywords | ||
| Alzheimer’s Disease Resting-State Functional Magnetic Resonance Imaging Blood-Oxygen-Level-Dependent Signal Artificial Neural Network Deep Learning | ||
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How to Cite
1.
Alizadeh F, Homayoun H, Batouli A hossein, Noroozian M, Sodaie F, Salary H, Kazerooni A, Saligheh Rad H. Differential Diagnosis Among Alzheimer’s Disease, Mild Cognitive Impairment, and Normal Subjects Using Resting-State fMRI Data Extracted from Multi Subject Dictionary Learning Atlas. Frontiers Biomed Technol. 2022;9(4):297-306.
