Deep networks for behavioral variant frontotemporal dementia identification from multiple acquisition sources

Logroscino, 2019, Amyotrophic lateral sclerosis descriptive epidemiology: the origin of geographic difference, Neuroepidemiology, 52, 93, 10.1159/000493386 Ratnavalli, 2002, The prevalence of frontotemporal dementia, Neurology, 58, 1615, 10.1212/WNL.58.11.1615 Onyike, 2013, The epidemiology of frontotemporal dementia, Int. Rev. Psych., 25, 130, 10.3109/09540261.2013.776523 Rascovsky, 2011, Sensitivity of revised diagnostic criteria for the behavioural variant of frontotemporal dementia, Brain, 134, 2456, 10.1093/brain/awr179 McCarthy, 2018, Morphometric MRI as a diagnostic biomarker of frontotemporal dementia: A systematic review to determine clinical applicability, NeuroImage: Clin., 20, 685, 10.1016/j.nicl.2018.08.028 Möller, 2016, Alzheimer disease and behavioral variant frontotemporal dementia: automatic classification based on cortical atrophy for single-subject diagnosis, Radiology, 279, 838, 10.1148/radiol.2015150220 Spasov, 2019, A parameter-efficient deep learning approach to predict conversion from mild cognitive impairment to Alzheimer’s disease, Neuroimage, 189, 276, 10.1016/j.neuroimage.2019.01.031 Basaia, 2019, Automated classification of Alzheimer’s disease and mild cognitive impairment using a single MRI and deep neural networks, NeuroImage: Clin., 21 Hu, 2021, Deep learning-based classification and voxel-based visualization of frontotemporal dementia and Alzheimer’s disease, Front. Neurosci., 14, 1468, 10.3389/fnins.2020.626154 Raamana, 2014, Three-class differential diagnosis among Alzheimer disease, frontotemporal dementia, and controls, Fronti. Neurol., 5, 71 Chow, 2008, Overlap in frontotemporal atrophy between normal aging and patients with frontotemporal dementias, Alzheimer Dis. Assoc. Disorders, 22, 327, 10.1097/WAD.0b013e31818026c4 Meyer, 2017, Predicting behavioral variant frontotemporal dementia with pattern classification in multi-center structural MRI data, NeuroImage: Clin., 14, 656, 10.1016/j.nicl.2017.02.001 Bachli, 2020, Evaluating the reliability of neurocognitive biomarkers of neurodegenerative diseases across countries: A machine learning approach, NeuroImage, 208, 10.1016/j.neuroimage.2019.116456 Donnelly-Kehoe, 2019, Robust automated computational approach for classifying frontotemporal neurodegeneration: Multimodal/multicenter neuroimaging, Alzheimer’s Dementia: Diagn. Assess. Dis. Monitoring, 11, 588 Nemoto, 2021, Differentiating dementia with lewy bodies and Alzheimer’s disease by deep learning to structural MRI, J. Neuroimaging, 31, 579, 10.1111/jon.12835 Qiu, 2020, Development and validation of an interpretable deep learning framework for Alzheimer’s disease classification, Brain, 143, 1920, 10.1093/brain/awaa137 Tufail, 2020, Binary classification of Alzheimer’s disease using sMRI imaging modality and deep learning, J. Digital Imaging, 33, 1073, 10.1007/s10278-019-00265-5 Ma, 2020, Differential diagnosis of frontotemporal dementia, Alzheimer’s disease, and normal aging using a multi-scale multi-type feature generative adversarial deep neural network on structural magnetic resonance images, Front. Neurosci., 14, 853, 10.3389/fnins.2020.00853 Lian, 2018, Hierarchical fully convolutional network for joint atrophy localization and Alzheimer’s disease diagnosis using structural MRI, IEEE Trans. Pattern Anal. Mach. Intell., 42, 880, 10.1109/TPAMI.2018.2889096 Gong, 2021, Optimising a simple fully convolutional network for accurate brain age prediction in the pac 2019 challenge, Front. Psych., 12 Vapnik, 2013 Cortes, 1995, Support-vector networks, Mach. Learn., 20, 273, 10.1007/BF00994018 Breiman, 2001, Random forests, Mach. Learn., 45, 5, 10.1023/A:1010933404324 Krizhevsky, 2012, Imagenet classification with deep convolutional neural networks, Adv. Neural Inf. Process. Syst., 25, 1097 Rosenblatt, 1961 Rosenblatt, 1958, The perceptron: a probabilistic model for information storage and organization in the brain, Psychol. Rev., 65, 386, 10.1037/h0042519 Udupa, 1999 Elman, 1990, Finding structure in time, Cogn. Sci., 14, 179, 10.1207/s15516709cog1402_1 Hochreiter, 1997, Long short-term memory, Neural Comput., 9, 1735, 10.1162/neco.1997.9.8.1735 Vaswani, 2017, Attention is all you need, 5998 Dosovitskiy, 2021 Wu, 2020 Tolstikhin, 2021 Liu, 2021 2021 Luo, 2003, Diagnosis and exploration of massively univariate neuroimaging models, NeuroImage, 19, 1014, 10.1016/S1053-8119(03)00149-6 2021 Schrouff, 2013, PRoNTo: pattern recognition for neuroimaging toolbox, Neuroinformatics, 11, 319, 10.1007/s12021-013-9178-1 Lee-Thorp, 2021