Querfurth HW, Laferla FM (2010) Alzheimer’s disease. N Engl J Med 362(4):329
Article CAS PubMed Google Scholar
Logothetis NK (2008) What we can do and what we cannot do with fMRI. Nature 453(7197):869–878
Article CAS PubMed Google Scholar
Oishi K, Mielke MM, Albert M, Lyketsos CG, Mori S (2011) DTI analyses and clinical applications in Alzheimer’s disease. J Alzheimers Dis 26(s3):287–296
Article PubMed PubMed Central Google Scholar
Zhang N, Gordon ML, Goldberg TE (2017) Cerebral blood flow measured by arterial spin labeling MRI at resting state in normal aging and Alzheimer’s disease. Neurosci Biobehav Rev 72:168–175
Article CAS PubMed Google Scholar
Liu M, Li F, Yan H, Wang K, Ma Y, Shen L, Xu M, AsDN I (2020) A multi-model deep convolutional neural network for automatic hippocampus segmentation and classification in Alzheimer’s disease. Neuroimage 208:116459
Logothetis NK, Pauls J, Augath M, Trinath T, Oeltermann A (2001) Neurophysiological investigation of the basis of the fMRI signal. Nature 412(6843):150–157
Article CAS PubMed Google Scholar
Zhao K, Ding Y, Han Y, Fan Y, Alexander-Bloch AF, Han T, Jin D, Liu B, Lu J, Song C (2020) Independent and reproducible hippocampal radiomic biomarkers for multisite Alzheimer’s disease: diagnosis, longitudinal progress and biological basis. Sci Bull 65(13):1103–1113
Zhang Y, Teng Q, Liu Y, Liu Y, He X (2022) Diagnosis of Alzheimer’s disease based on regional attention with sMRI gray matter slices. J Neurosci Method 365:109376
Han K, Wang Y, Chen H, Chen X, Guo J, Liu Z, Tang Y, Xiao A, Xu C, Xu Y (2022) A survey on vision transformer. IEEE Trans Pattern Anal Mach Intell 45(1):87–110
Dai W, Zhang Z, Tian L, Yu S, Wang S, Dong Z, Zheng H (2022) BrainFormer: a hybrid CNN-transformer model for brain fMRI data classification. arXiv preprint arXiv:220803028.
Alexander-Bloch A, Giedd JN, Bullmore E (2013) Imaging structural co-variance between human brain regions. Nat Rev Neurosci 14(5):322–336
Article CAS PubMed PubMed Central Google Scholar
Zhao K, Zheng Q, Che T, Dyrba M, Li Q, Ding Y, Zheng Y, Liu Y, Li S (2021) Regional radiomics similarity networks (R2SNs) in the human brain: reproducibility, small-world properties and a biological basis. Netw Neurosci 5(3):783–797
PubMed PubMed Central Google Scholar
Zhao K, Zheng Q, Dyrba M, Rittman T, Li A, Che T, Chen P, Sun Y, Kang X, Li Q (2022) Regional radiomics similarity networks reveal distinct subtypes and abnormality patterns in mild cognitive impairment. Adv Sci 9(12):2104538
Yao D, Sui J, Yang E, Yap P, Shen D, Liu M (2020) Temporal-adaptive graph convolutional network for automated identification of major depressive disorder using resting-state fMRI. International workshop on machine learning in medical imaging. Springer, New York, pp 1–10
Yao D, Sui J, Wang M, Yang E, Jiaerken Y, Luo N, Yap P-T, Liu M, Shen D (2021) A mutual multi-scale triplet graph convolutional network for classification of brain disorders using functional or structural connectivity. IEEE Trans Med Imaging 40(4):1279–1289
Article PubMed PubMed Central Google Scholar
Wang J, Knol MJ, Tiulpin A, Dubost F, de Bruijne M, Vernooij MW, Adams HH, Ikram MA, Niessen WJ, Roshchupkin GV (2019) Gray matter age prediction as a biomarker for risk of dementia. Proc Natl Acad Sci 116(42):21213–21218
Article CAS PubMed PubMed Central Google Scholar
He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770–778
Dosovitskiy A, Beyer L, Kolesnikov A, Weissenborn D, Zhai X, Unterthiner T, Dehghani M, Minderer M, Heigold G, Gelly S (2020) An image is worth 16x16 words: transformers for image recognition at scale. arXiv preprint arXiv:201011929
Guo J, Han K, Wu H, Tang Y, Chen X, Wang Y, Xu C (2022) Cmt: Convolutional neural networks meet vision transformers. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 12175–12185
Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser Ł, Polosukhin I (2017) Attention is all you need. Advances in Neural Information Processing Systems 30
Hamilton W, Ying Z, Leskovec J (2017) Inductive representation learning on large graphs. Advances in Neural Information Processing Systems 30
Khoshraftar S, An A (2024) A survey on graph representation learning methods. ACM Trans Intell Syst Technol 15(1):1–55
Zhao M, Yan W, Luo N, Zhi D, Fu Z, Du Y, Yu S, Jiang T, Calhoun VD, Sui J (2022) An attention-based hybrid deep learning framework integrating brain connectivity and activity of resting-state functional MRI data. Med Image Anal 78:102413
Article PubMed PubMed Central Google Scholar
Sun L, Peng Q, Qian C, Li J Tau (2022) Content prediction based on brain network MLP-att model. In: 2022 international conference on machine learning, cloud computing and intelligent mining (MLCCIM). IEEE, pp 353–358
Zhang S, Chen X, Shen X, Ren B, Yu Z, Yang H, Jiang X, Shen D, Zhou Y, Zhang X-Y (2023) A-GCL: Adversarial graph contrastive learning for fMRI analysis to diagnose neurodevelopmental disorders. Med Image Anal 90:102932
Xia M, Wang J, He Y (2013) BrainNet Viewer: a network visualization tool for human brain connectomics. PLoS ONE 8(7):e68910
Article CAS PubMed PubMed Central Google Scholar
Wang J, Wang X, Xia M, Liao X, Evans A, He Y (2015) GRETNA: a graph theoretical network analysis toolbox for imaging connectomics. Front Hum Neurosci 9:386
CAS PubMed PubMed Central Google Scholar
Zhang Y, Li H, Zheng Q (2023) A comprehensive characterization of hippocampal feature ensemble serves as individualized brain signature for Alzheimer’s disease: deep learning analysis in 3238 participants worldwide. Eur Radiol 33(8):5385–5397
Jin D, Zhou B, Han Y, Ren J, Han T, Liu B, Lu J, Song C, Wang P, Wang D (2020) Generalizable, reproducible, and neuroscientifically interpretable imaging biomarkers for Alzheimer’s disease. Adv Sci 7(14):2000675
Ebrahimi A, Luo S, Chiong R (2020) Introducing transfer learning to 3D ResNet-18 for Alzheimer’s disease detection on MRI images. In: 2020 35th international conference on image and vision computing New Zealand (IVCNZ), Wellington, New Zealand. IEEE, pp 1–6
AI-Tam RM, AI-Hejri AM, Narangale SM, Samee NA, Mahmoud NF, AI-Masni MA, AI-Antari MA (2022) A hybrid workflow of residual convolutional transformer encoder for breast cancer classificationi using digital X-ray mammograms. Biomedicines 10(11):1971
Kipf TN, Welling M (2016) Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:160902907
Taud H, Mas J (2018) Multilayer perceptron (MLP). In: Geomatic approaches for modeling land change scenarios. Springer, pp 451–455
Veličković P, Cucurull G, Casanova A, Romero A, Lio P, Bengio Y (2017) Graph attention networks. arXiv preprint arXiv:171010903
Hu W, Liu B, Gomes J, Zitnik M, Liang P, Pande V, Leskovec J (2019) Strategies for pre-training graph neural networks. arXiv preprint arXiv:190512265
Kong Z, Zhang M, Zhu W, Yi Y, Wang T, Zhang B (2022) Multi-modal data Alzheimer’s disease detection based on 3D convolution. Biomed Signal Process Control 75:103565
Pan J, Lei B, Shen Y, Liu Y, Feng Z, Wang S (2021) Characterization multimodal connectivity of brain network by hypergraph GAN for Alzheimer’s disease analysis. Chinese conference on pattern recognition and computer vision (PRCV). Springer, pp 467–478
Rasheed K, Qayyum A, Ghaly M, Al-Fuqaha A, Razi A, Qadir J (2022) Explainable, trustworthy, and ethical machine learning for healthcare: a survey. Comput Biol Med 149:106043
Qiu S, Joshi PS, Miller MI, Xue C, Zhou X, Karjadi C, Chang GH, Joshi AS, Dwyer B, Zhu S (2020) Development and validation of an interpretable deep learning framework for Alzheimer’s disease classification. Brain 143(6):1920–1933
Article PubMed PubMed Central Google Scholar
Bäckman L, Andersson J, Nyberg L, Winblad B, Nordberg A, Almkvist O (1999) Brain regions associated with episodic retrieval in normal aging and Alzheimer’s disease. Neurology 52(9):1861–1861
Zhang Y, Wang S, Phillips P, Yang J, Yuan T-F (2016) Three-dimensional eigenbrain for the detection of subjects and brain regions related with Alzheimer’s disease. J Alzheimers Dis 50(4):1163–1179
Ding Y, Sohn JH, Kawczynski MG, Trivedi H, Harnish R, Jenkins NW, Lituiev D, Copeland TP, Aboian MS, Mari Aparici C (2019) A deep learning model to predict a diagnosis of Alzheimer disease by using 18F-FDG PET of the brain. Radiology 290(2):456–464
Caso F, Agosta F, Mattavelli D, Migliaccio R, Canu E, Magnani G, Marcone A, Copetti M, Falautano M, Comi G (2015) White matter degeneration in atypical Alzheimer disease. Radiology 277(1):162–172
Möller C, Pijnenburg YA, van der Flier WM, Versteeg A, Tijms B, de Munck JC, Hafkemeijer A, Rombouts SA, van der Grond J, van Swieten J (2016) Alzheimer disease and behavioral variant frontotemporal dementia: automatic classification based on cortical atrophy for single-subject diagnosis. Radiology 279(3):838–848
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