Alzheimer Disease and Behavioral Variant Frontotemporal Dementia: Automatic Classification Based on Cortical Atrophy for Single-Subject Diagnosis
Open accessAbstract
Purpose To investigate the diagnostic accuracy of an image-based classifier to distinguish between Alzheimer disease (AD) and behavioral variant frontotemporal dementia (bvFTD) in individual patients by using gray matter (GM) density maps computed from standard T1-weighted structural images obtained with multiple imagers and with independent training and prediction data. Materials and Methods The local institutional review board approved the study. Eighty-four patients with AD, 51 patients with bvFTD, and 94 control subjects were divided into independent training (n = 115) and prediction (n = 114) sets with identical diagnosis and imager type distributions. Training of a support vector machine (SVM) classifier used diagnostic status and GM density maps and produced voxelwise discrimination maps. Discriminant function analysis was used to estimate suitability of the extracted weights for single-subject classification in the prediction set. Receiver operating characteristic (ROC) curves and area under the ROC curve (AUC) were calculated for image-based classifiers and neuropsychological z scores. Results Training accuracy of the SVM was 85% for patients with AD versus control subjects, 72% for patients with bvFTD versus control subjects, and 79% for patients with AD versus patients with bvFTD (P ≤ .029). Single-subject diagnosis in the prediction set when using the discrimination maps yielded accuracies of 88% for patients with AD versus control subjects, 85% for patients with bvFTD versus control subjects, and 82% for patients with AD versus patients with bvFTD, with a good to excellent AUC (range, 0.81-0.95; P ≤ .001). Machine learning-based categorization of AD versus bvFTD based on GM density maps outperforms classification based on neuropsychological test results. Conclusion The SVM can be used in single-subject discrimination and can help the clinician arrive at a diagnosis. The SVM can be used to distinguish disease-specific GM patterns in patients with AD and those with bvFTD as compared with normal aging by using common T1-weighted structural MR imaging. (©) RSNA, 2015.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Machine learning techniques have been applied to distinguish bvFTD from cognitively normal controls, Alzheimer Disease or other psychiatric and neurologic disorders on an individual level using MRI-based features.
“Recently, machine learning techniques have been applied to distinguish between bvFTD and Cognitively Normal Subjects (CNCs), Alzheimer Disease or other psychiatric and neurologic disorders on an individual level using MRI-based features. 9-17”
From MRI data-driven algorithm for the diagnosis of behavioural variant frontotemporal dementia (2021) · cites this paper as [12]
The Training-set results (p. 11) report MRI-based SVM classification of bvFTD versus controls and versus Alzheimer disease; the paper also describes its approach as single-subject classification (Radiology, p. 15). It does not report distinguishing bvFTD from other psychiatric or neurologic disorders: the Discussion (p. 14) says it used only binary classifiers. (LLM, conf=0.88)
Experimental — accuracy not yet measuredPrior machine learning studies achieved moderate to high accuracy distinguishing bvFTD.
“In general, they achieved moderate to high accuracy distinguishing bvFTD.”
From MRI data-driven algorithm for the diagnosis of behavioural variant frontotemporal dementia (2021) · cites this paper as [12]
Previous studies classified bvFTD from a control group.
“Previous studies classified bvFTD from a control group. 9-13”
From MRI data-driven algorithm for the diagnosis of behavioural variant frontotemporal dementia (2021) · cites this paper as [12]
Machine-learning algorithms using MRI volumetry to develop diagnostic classifiers fare well against controls but achieve maximal performance of 82% in separating well-characterized cases of bvFTD from Alzheimer's disease.
“Machine-learning algorithms using MRI volumetry to develop diagnostic classifiers fare well against controls but achieve maximal performance of 82% in separating wellcharacterized cases of bvFTD from Alzheimer's disease (Moller et al. , 2016; Canu et al. , 2017; McCarthy et al. , 2018).”
From Recommendations to distinguish behavioural variant frontotemporal dementia from psychiatric disorders (2020) · cites this paper as Moller et al. , 2016
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Folstein, M. F., Folstein, S. E., & McHugh, P. R. (1975) · Journal of Psychiatric Research · doi:10.1016/0022-3956(75)90026-6 · Cited by 88,282 · see citing works
McKhann, G., Drachman, D., Folstein, M., Katzman, R., Price, D., & Stadlan, E. M. (1984) · Neurology · doi:10.1212/wnl.34.7.939 · Cited by 27,915 · see citing works
McKhann, G. M., Knopman, D. S., Chertkow, H., Hyman, B. T., Jack, C. R., Kawas, C. H., Klunk, W. E., Koroshetz, W. J., Manly, J. J., Mayeux, R., et al. (2011) · Alzheimer's & Dementia · doi:10.1016/j.jalz.2011.03.005 · Cited by 18,657 · see citing works
Self-citationStudies of interference in serial verbal reactions
Stroop, J. R. (1935) · Journal of Experimental Psychology · doi:10.1037/h0054651 · Cited by 18,622 · see citing works
A global optimisation method for robust affine registration of brain images
Jenkinson, M., & Smith, S. M. (2001) · Medical Image Analysis · doi:10.1016/s1361-8415(01)00036-6 · Cited by 6,806 · see citing works
Sensitivity of revised diagnostic criteria for the behavioural variant of frontotemporal dementia
Rascovsky, K., Hodges, J. R., Knopman, D., Mendez, M. F., Kramer, J. H., Neuhaus, J., van Swieten, J. C., Seelaar, H., Dopper, E. G. P., Onyike, C. U., et al. (2011) · Brain · doi:10.1093/brain/awr179 · Cited by 5,221 · see citing works
Self-citationThe FAB: A frontal assessment battery at bedside
Dubois, B., Slachevsky, A., Litvan, I., & Pillon, B. (2000) · Neurology · doi:10.1212/wnl.55.11.1621 · Cited by 3,913 · see citing works
Petersen, R. C., Stevens, J. C., Ganguli, M., Tangalos, E. G., Cummings, J. L., & DeKosky, S. T. (2001) · Neurology · doi:10.1212/wnl.56.9.1133 · Cited by 2,065 · see citing works
Multimodal classification of Alzheimer's disease and mild cognitive impairment
Zhang, D., Wang, Y., Zhou, L., Yuan, H., & Shen, D. G. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.01.008 · Cited by 1,312 · see citing works
Automatic classification of MR scans in Alzheimer’s disease.
Kloppel, S., Stonnington, C. M., Chu, C., Draganski, B., Scahill, R. I., Rohrer, J. D., Fox, N. C., Jack, C. R., Ashburner, J., & Frackowiak, R. S. J. (2008) · Brain · doi:10.1093/brain/awm319 · Cited by 1,170 · see citing works
The prevalence of frontotemporal dementia
Ratnavalli, E., Brayne, C., Dawson, K., & Hodges, J. R. (2002) · Neurology · doi:10.1212/wnl.58.11.1615 · Cited by 1,151 · see citing works
Galton, C. J. (2000) · Brain · doi:10.1093/brain/123.3.484 · Cited by 613 · see citing works
Different regional patterns of cortical thinning in Alzheimer's disease and frontotemporal dementia
Du, A., Schuff, N. W., Kramer, J. H., Rosen, H. J., Gorno‐Tempini, M. L., Rankin, K. P., Miller, B. L., & Weiner, M. W. (2006) · Brain · doi:10.1093/brain/awm016 · Cited by 479 · see citing works
Scheltens, P., Launer, L. J., Barkhof, F., Weinstein, H. C., & van Gool, W. A. (1995) · Journal of Neurology · doi:10.1007/bf00868807 · Cited by 470 · see citing works
Self-citationEarly-Versus Late-Onset Alzheimer's Disease: More than Age Alone
Koedam, E. L. G. E., Lauffer, V., van der Vlies, A. E., van der Flier, W. M., Scheltens, P., & Pijnenburg, Y. A. L. (2010) · Journal of Alzheimer's Disease · doi:10.3233/jad-2010-1337 · Cited by 452 · see citing works
Self-citationPRoNTo: Pattern Recognition for Neuroimaging Toolbox
Schrouff, J., Rosa, M. J., Rondina, J. M., Marquand, A. F., Chu, C., Ashburner, J., Phillips, C. L. M., Richiardi, J., & Mourão-Miranda, J. (2013) · Neuroinformatics · doi:10.1007/s12021-013-9178-1 · Cited by 424 · see citing works
Clinical and Pathological Evidence for a Frontal Variant of Alzheimer Disease
Johnson, J. K., Head, E., Kim, R., Starr, A., & Cotman, C. W. (1999) · Archives of Neurology · doi:10.1001/archneur.56.10.1233 · Cited by 414 · see citing works
Visual assessment of posterior atrophy development of a MRI rating scale
Koedam, E., Lehmann, M., van der Flier, W. M., Scheltens, P., Pijnenburg, Y. A. L., Fox, N. C., Barkhof, F., & Wattjes, M. P. (2011) · European Radiology · doi:10.1007/s00330-011-2205-4 · Cited by 410 · see citing works
Self-citationOptimizing Patient Care and Research: The Amsterdam Dementia Cohort
van der Flier, W. M., Pijnenburg, Y. A. L., Prins, N. D., Lemstra, A. W., Bouwman, F. H., Teunissen, C. E., van Berckel, B. N. M., Stam, C. J., Barkhof, F., Visser, P. J., et al. (2014) · Journal of Alzheimer s Disease · doi:10.3233/jad-132306 · Cited by 390 · see citing works
Self-citationAutomated detection of brain atrophy patterns based on MRI for the prediction of Alzheimer's disease
Plant, C., Teipel, S. J., Oswald, A., Böhm, C., Meindl, T., Mourão-Miranda, J., Bokde, A. L. W., Hampel, H. J., & Ewers, M. (2009) · NeuroImage · doi:10.1016/j.neuroimage.2009.11.046 · Cited by 341 · see citing works
Accuracy of dementia diagnosis: a direct comparison between radiologists and a computerized method
Kloppel, S., Stonnington, C. M., Barnes, J., Chen, F., Chu, C., Good, C. D., Mader, I., Mitchell, L. A., Patel, A. C., Roberts, C. C., et al. (2008) · Brain · doi:10.1093/brain/awn239 · Cited by 271 · see citing works
Rabinovici, G. D., Seeley, W. W., Kim, E. J., Gorno‐Tempini, M. L., Rascovsky, K., Pagliaro, T. A., Allison, S. C., Halabi, C., Kramer, J. H., Johnson, J. K., et al. (2007) · American Journal of Alzheimer s Disease & Other Dementias® · doi:10.1177/1533317507308779 · Cited by 256 · see citing works
Intelligentie en leeftijd : onderzoek bij Nederlanders van twaalf tot zevenenzeventig jaar
Verhage, F. (1964) · Van Gorcum eBooks · Cited by 245 · see citing works
Individual patient diagnosis of AD and FTD via high-dimensional pattern classification of MRI
Davatzikos, C. A., Resnick, S. M., Wu, X., Parmpi, P., & Clark, C. (2008) · NeuroImage · doi:10.1016/j.neuroimage.2008.03.050 · Cited by 240 · see citing works
Hippocampal atrophy on MRI in frontotemporal lobar degeneration and Alzheimer’s disease
van de Pol, L. A., Hensel, A., van der Flier, W. M., Visser, P., Pijnenburg, Y., Barkhof, F., Gertz, H.-J., & Scheltens, P. (2005) · Journal of Neurology Neurosurgery & Psychiatry · doi:10.1136/jnnp.2005.075341 · Cited by 223 · see citing works
Self-citationEpisodic memory in frontotemporal dementia: a critical review
Hornberger, M., & Piguet, O. (2012) · Brain · doi:10.1093/brain/aws011 · Cited by 210 · see citing works
Hutchinson, A. D., & Mathias, J. L. (2007) · Journal of Neurology, Neurosurgery & Psychiatry · doi:10.1136/jnnp.2006.100669 · Cited by 187 · see citing works
Early onset Alzheimer’s disease is associated with a distinct neuropsychological profile
Smits, L. L., Pijnenburg, Y. A. L., Koedam, E. L. G. E., van der Vlies, A. E., Reuling, I. E. W., Koene, T., Teunissen, C. E., Scheltens, P., & van der Flier, W. M. (2012) · Journal of Alzheimer s Disease · doi:10.3233/jad-2012-111934 · Cited by 182 · see citing works
Self-citationCombined Evaluation of FDG-PET and MRI Improves Detection and Differentiation of Dementia
Dukart, J., Mueller, K., Horstmann, A., Barthel, H., Möller, H. E., Villringer, A., Sabri, O., & Schroeter, M. L. (2011) · PLoS ONE · doi:10.1371/journal.pone.0018111 · Cited by 153 · see citing works
MRI-Based Automated Computer Classification of Probable AD Versus Normal Controls
Duchesne, S., Caroli, A., Geroldi, C., Barillot, C., Frisoni, G. B., & Collins, D. L. (2008) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2007.908685 · Cited by 150 · see citing works
Dickerson, B. C., Wolk, D. A., & Initiative, T. A. D. N. (2010) · Journal of Neurology Neurosurgery & Psychiatry · doi:10.1136/jnnp.2009.199505 · Cited by 139 · see citing works
Magnetic Resonance Imaging Signatures of Tissue Pathology in Frontotemporal Dementia
Whitwell, J. L., Josephs, K. A., Rossor, M. N., Stevens, J., Révész, T., Holton, J. L., Al‐Sarraj, S., Godbolt, A. K., Fox, N. C., & Warren, J. D. (2005) · Archives of Neurology · doi:10.1001/archneur.62.9.1402 · Cited by 137 · see citing works
Mourão-Miranda, J., Reinders, A. A. T. S., Rocha-Rego, V., Lappin, J., Rondina, J. M., Morgan, C., Morgan, K. D., Fearon, P., Jones, P. B., Doody, G. A., et al. (2011) · Psychological Medicine · doi:10.1017/s0033291711002005 · Cited by 130 · see citing works
Antemortem differential diagnosis of dementia pathology using structural MRI: Differential-STAND
Vemuri, P., Simon, G., Kantarci, K., Whitwell, J. L., Senjem, M. L., Przybelski, S. A., Gunter, J. L., Josephs, K. A., Knopman, D. S., Boeve, B. F., et al. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2010.12.073 · Cited by 108 · see citing works
Two Distinct Amnesic Profiles in Behavioral Variant Frontotemporal Dementia
Bertoux, M., de Souza, L. C., Corlier, F. W., Lamari, F., Bottlaender, M., Dubois, B., & Sarazin, M. (2013) · Biological Psychiatry · doi:10.1016/j.biopsych.2013.08.017 · Cited by 104 · see citing works
Bron, E. E., Steketee, R. M. E., Houston, G. C., Oliver, R. A., Achterberg, H. C., Loog, M., van Swieten, J. C., Hammers, A., Niessen, W. J., Smits, M., et al. (2014) · Human Brain Mapping · doi:10.1002/hbm.22522 · Cited by 93 · see citing works
Presenile dementia syndromes: an update on taxonomy and diagnosis
Greicius, M. D. (2002) · Journal of Neurology Neurosurgery & Psychiatry · doi:10.1136/jnnp.72.6.691 · Cited by 85 · see citing works
Castiglioni, S., Pelati, O., Zuffi, M., Somalvico, F., Marino, L., Tentorio, T., & Franceschi, M. (2006) · Dementia and Geriatric Cognitive Disorders · doi:10.1159/000093665 · Cited by 83 · see citing works
Whitwell, J. L., & Jack, C. R. (2005) · Topics in Magnetic Resonance Imaging · doi:10.1097/01.rmr.0000245457.98029.e1 · Cited by 80 · see citing works
Lost and Forgotten? Orientation Versus Memory in Alzheimer's Disease and Frontotemporal Dementia
Yew, B., Alladi, S., Mekala, S., Hodges, J. R., & Hornberger, M. (2012) · Journal of Alzheimer s Disease · doi:10.3233/jad-2012-120769 · Cited by 74 · see citing works
Möller, C., Dieleman, N., van der Flier, W. M., Versteeg, A., Pijnenburg, Y. A. L., Scheltens, P., Barkhof, F., & Vrenken, H. (2015) · Journal of Alzheimer s Disease · doi:10.3233/jad-141230 · Cited by 58 · see citing works
Self-citationHorn, J.-F., Habert, M., Kas, A., Malek, Z., Maksud, P., Lacomblez, L., Giron, A., & Fertil, B. (2009) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2009.05.001 · Cited by 55 · see citing works
Three-Class Differential Diagnosis among Alzheimer Disease, Frontotemporal Dementia, and Controls
Raamana, P. R., Rosen, H., Miller, B., Weiner, M. W., Wang, L., & Beg, M. F. (2014) · Frontiers in Neurology · doi:10.3389/fneur.2014.00071 · Cited by 55 · see citing works
Miller, B. L., Diehl, J., Freedman, M., KERTESZ, A. E., Mendez, M. F., & Rascovsky, K. (2003) · Annals of Neurology · doi:10.1002/ana.10568 · Cited by 34 · see citing works
Jenner, C., Reali, G., Puopolo, M., & Silveri, M. C. (2006) · Behavioural Neurology · doi:10.1155/2006/812760 · Cited by 33 · see citing works
Automated Identification of Dementia Using FDG-PET Imaging
Xia, Y., Shen, L., Wen, L., Eberl, S., FULHAM, M. J., & Feng, D. D. (2014) · BioMed Research International · doi:10.1155/2014/421743 · Cited by 21 · see citing works
Christensen (1997) · doi:10.1002/(sici)1099-1166(199703)12:3
Flagged first, then by citations
Cited by
Sorted by citations
Rathore, S., Habes, M., Iftikhar, M. A., Shacklett, A., & Davatzikos, C. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.03.057 · Cited by 622 · see citing works
Lian, C., Liu, M., Zhang, J., & Shen, D. (2020) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2018.2889096 · Cited by 544 · see citing works
Amsterdam Dementia Cohort: Performing Research to Optimize Care
van der Flier, W. M., & Scheltens, P. (2018) · Journal of Alzheimer s Disease · doi:10.3233/jad-170850 · Cited by 379 · see citing works
Self-citationMachine Learning Techniques for the Diagnosis of Alzheimer’s Disease
Tanveer, M., Richhariya, B., Khan, R. U., Rashid, A. H., Khanna, P., Prasad, M., & Lin, C. (2020) · ACM Transactions on Multimedia Computing Communications and Applications · doi:10.1145/3344998 · Cited by 334 · see citing works
Ducharme, S., Dols, A., Laforce, R., Devenney, E., Kumfor, F., van den Stock, J., Dallaire-Théroux, C., Seelaar, H., Gossink, F., Vijverberg, E., et al. (2020) · Brain · doi:10.1093/brain/awaa018 · Cited by 320 · see citing works
Self-citationZhang, X., Han, L., Zhu, W., Sun, L., & Zhang, D. (2021) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2021.3066832 · Cited by 208 · see citing works
Cortical graph neural network for AD and MCI diagnosis and transfer learning across populations
Chong-yaw Wee, A., Liu, C., Lee, A., Poh, J. S., Ji, H., & Qiu, A. (2019) · NeuroImage Clinical · doi:10.1016/j.nicl.2019.101929 · Cited by 146 · see citing works
Frizzell, T. O., Glashutter, M., Liu, C. C., Zeng, A., Pan, D., Hajra, S. G., D’Arcy, R. C. N., & Song, X. (2022) · Ageing Research Reviews · doi:10.1016/j.arr.2022.101614 · Cited by 139 · see citing works
Artificial intelligence for diagnostic and prognostic neuroimaging in dementia: a systematic review
Borchert, R. J., Azevedo, T., Badhwar, A., Bernal, J., Betts, M., Bruffaerts, R., Burkhart, M. C., Dewachter, I., Gellersen, H. M., Low, A., et al. (2023) · Alzheimer's & Dementia · doi:10.1002/alz.13412 · Cited by 125 · see citing works
Multiparametric ultrasomics of significant liver fibrosis: A machine learning-based analysis
Li, W., Huang, Y., Zhuang, B., Liu, G., Hu, H.-T., Li, X., Liang, J., Wang, Z., Huang, X.-W., Zhang, C.-Q., et al. (2018) · European Radiology · doi:10.1007/s00330-018-5680-z · Cited by 119 · see citing works
Kim, J. P., Kim, J., Park, Y. H., Park, S. B., Lee, J. S., Yoo, S., Kim, E.-J., Kim, H. J., Na, D. L., Brown, J. A., et al. (2019) · NeuroImage: Clinical · doi:10.1016/j.nicl.2019.101811 · Cited by 100 · see citing works
Deep Fusion of Brain Structure-Function in Mild Cognitive Impairment
Zhang, L., Wang, L., Gao, J., Risacher, S. L., Yan, J., Li, G., Liu, T., & Zhu, D. (2021) · Medical Image Analysis · doi:10.1016/j.media.2021.102082 · Cited by 96 · see citing works
Meyer, S., Mueller, K., Stuke, K., Bisenius, S., Diehl-Schmid, J., Jessen, F., Kassubek, J., Kornhuber, J., Ludolph, A. C., Prudlo, J., et al. (2017) · NeuroImage: Clinical · doi:10.1016/j.nicl.2017.02.001 · Cited by 83 · see citing works
Bron, E. E., Smits, M., Papma, J. M., Steketee, R. M. E., Meijboom, R., de Groot, M., van Swieten, J. C., Niessen, W. J., & Klein, S. (2017) · European Radiology · doi:10.1007/s00330-016-4691-x · Cited by 84 · see citing works
Abbas, S. Q., Chi, L., & Chen, Y. P. (2022) · Pattern Recognition · doi:10.1016/j.patcog.2022.109031 · Cited by 81 · see citing works
A transformer-based unified multimodal framework for Alzheimer's disease assessment
Yu, Q., Ma, Q., Da, L., Li, J., Wang, M., Xu, A., Li, Z., & Li, W. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.108979 · Cited by 82 · see citing works
Detecting frontotemporal dementia syndromes using MRI biomarkers
Bruun, M., Koikkalainen, J., Rhodius-Meester, H., Baroni, M., Gjerum, L., van Gils, M., Soininen, H., Remes, A., Hartikainen, P., Waldemar, G., et al. (2019) · NeuroImage: Clinical · doi:10.1016/j.nicl.2019.101711 · Cited by 66 · see citing works
Self-citationRandom support vector machine cluster analysis of resting-state fMRI in Alzheimer's disease
Bi, X.-A., Ma, S. Q., Sun, Q., & Xu, Q. (2018) · PLoS ONE · doi:10.1371/journal.pone.0194479 · Cited by 75 · see citing works
Lu, P., Hu, L., Mitelpunkt, A., Bhatnagar, S., Lu, L. J., & Liang, H. (2023) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2023.105669 · Cited by 73 · see citing works
Automated detection of Alzheimer’s Disease using Deep Learning in MRI
Raees, P. C. M., & Thomas, V. (2021) · Journal of Physics Conference Series · doi:10.1088/1742-6596/1921/1/012024 · Cited by 71 · see citing works
An MRI-based strategy for differentiation of frontotemporal dementia and Alzheimer’s disease
the National Alzheimer’s Coordinating Center, T. A. D. N. I., Yu, Q., Initiative, T. F. L. D. N., Mai, Y., Ruan, Y., Luo, Y., Zhao, L., Fang, W., Cao, Z., Li, Y., et al. (2021) · Alzheimer's Research & Therapy · doi:10.1186/s13195-020-00757-5 · Cited by 63 · see citing works
A Single Model Deep Learning Approach for Alzheimer’s Disease Diagnosis
Zhang, F., Pan, B., Shao, P., Liu, P., Shen, S., Yao, P., & Xu, R. X. (2022) · Neuroscience · doi:10.1016/j.neuroscience.2022.03.026 · Cited by 67 · see citing works
Bachli, M. B., Sedeño, L., Ochab, J. K., Piguet, O., Kumfor, F., Reyes, P., Torralva, T., Roca, M., Cardona, J. F., Campo, C. G., et al. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2019.116456 · Cited by 57 · see citing works
Shin, N., Bang, M., Yoo, S., Kim, J., Yun, E., Yoon, U., Han, K., Ahn, K. J., & Lee, S. (2021) · Radiology · doi:10.1148/radiol.2021203383 · Cited by 55 · see citing works
FTD spectrum: Neuroimaging across the FTD spectrum
Whitwell, J. L. (2019) · Progress in Molecular Biology and Translational Science · doi:10.1016/bs.pmbts.2019.05.009 · Cited by 53 · see citing works
Garg, N., Choudhry, M. S., & Bodade, R. M. (2022) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2022.109745 · Cited by 58 · see citing works
Amen, D., Willeumier, K. C., Omalu, B. I., Newberg, A. B., Raghavendra, C. S., & Raji, C. A. (2016) · Journal of Alzheimer s Disease · doi:10.3233/jad-160207 · Cited by 53 · see citing works
McCarthy, J., Collins, D. L., & Ducharme, S. (2018) · NeuroImage: Clinical · doi:10.1016/j.nicl.2018.08.028 · Cited by 47 · see citing works
Data-Driven Differential Diagnosis of Dementia Using Multiclass Disease State Index Classifier
Tolonen, A. J., Rhodius‐Meester, H. F. M., Bruun, M., Koikkalainen, J. R., Barkhof, F., Lemstra, A. W., Koene, T., Scheltens, P., Teunissen, C. E., Tong, T., et al. (2018) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2018.00111 · Cited by 44 · see citing works
Self-citationCerebrospinal fluid α‐synuclein contributes to the differential diagnosis of Alzheimer's disease
Shi, M., Tang, L., Toledo, J. B., Ginghina, C., Wang, H., Aro, P., Jensen, P. H., Weintraub, D., Chen‐Plotkin, A. S., Irwin, D. J., et al. (2018) · Alzheimer's & Dementia · doi:10.1016/j.jalz.2018.02.015 · Cited by 42 · see citing works
Ducharme, S., Pearl-Dowler, L., Gossink, F., McCarthy, J., Lai, J., Dickerson, B. C., Chertkow, H., Rapin, L., Vijverberg, E., Krudop, W., et al. (2019) · Journal of Alzheimer's Disease · doi:10.3233/jad-180839 · Cited by 42 · see citing works
Self-citationDeep grading for MRI-based differential diagnosis of Alzheimer’s disease and frontotemporal dementia
Nguyen, H.-D., Clément, M., Planche, V., Mansencal, B., & Coupé, P. (2023) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2023.102636 · Cited by 35 · see citing works
Zhang, X., Han, L., Han, L., Chen, H., Dancey, D., & Zhang, D. (2023) · IEEE Access · doi:10.1109/access.2023.3321220 · Cited by 35 · see citing works
Machine Learning Techniques to Identify Dementia
Mathkunti, N. M., & Rangaswamy, S. (2020) · SN Computer Science · doi:10.1007/s42979-020-0099-4 · Cited by 32 · see citing works
Chagué, P., Marro, B., Fadili, S., Houot, M., Morin, A., Samper-González, J., Beunon, P., Arrivé, L., Dormont, D., Dubois, B., et al. (2021) · Journal of Neuroradiology · doi:10.1016/j.neurad.2020.04.004 · Cited by 31 · see citing works
Feis, R. A., Bouts, M. J. R. J., Panman, J. L., Jiskoot, L. C., Dopper, E. G. P., Schouten, T. M., de Vos, F., van der Grond, J., Swieten, J. C. V., & Rombouts, S. A. R. B. (2018) · NeuroImage Clinical · doi:10.1016/j.nicl.2018.07.014 · Cited by 30 · see citing works
Self-citationVyas, A., Aisopos, F., Vidal, M.-E., Garrard, P., & Παλιούρας, Γ. (2022) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-022-02004-3 · Cited by 28 · see citing works
Pérez‐Millan, A., Contador, J., Juncà‐Parella, J., Bosch, B., Borrell, L., Tort‐Merino, A., Falgàs, N., Borrego‐Écija, S., Bargalló, N., Rami, L., et al. (2023) · Human Brain Mapping · doi:10.1002/hbm.26205 · Cited by 30 · see citing works
Eldaief, M. C., Brickhouse, M. J., Katsumi, Y., Rosen, H. J., Carvalho, N., Touroutoglou, A., & Dickerson, B. C. (2023) · Brain · doi:10.1093/brain/awad167 · Cited by 24 · see citing works
Francesco, S. D., Crema, C., Archetti, D., Muscio, C., Reid, R. I., Nigri, A., Bruzzone, M. G., Tagliavini, F., Lodi, R., D'Angelo, E. U., et al. (2023) · Scientific Reports · doi:10.1038/s41598-023-43706-6 · Cited by 26 · see citing works
MRI data-driven algorithm for the diagnosis of behavioural variant frontotemporal dementia
Manera, A. L., Dadar, M., Van Swieten, J. C., Borroni, B., Sanchez-Valle, R., Moreno, F., Laforce Jr, R., Graff, C., Synofzik, M., Galimberti, D., et al. (2021) · Journal of Neurology, Neurosurgery & Psychiatry · doi:10.1136/jnnp-2020-324106 · Cited by 23 · see citing works
Longitudinal Brain Atrophy Rates in Presymptomatic Carriers of Genetic Frontotemporal Dementia
Poos, J. M., Grandpierre, L. D. M., van der Ende, E. L., Panman, J. L., Papma, J. M., Seelaar, H., van den Berg, E., Klooster, R. V. ’., Bron, E. E., Steketee, R. M. E., et al. (2022) · Neurology · doi:10.1212/wnl.0000000000201292 · Cited by 24 · see citing works
Self-citationA multivariate metabolic imaging marker for behavioral variant frontotemporal dementia
Nazem, A., Tang, C. C., Spetsieris, P. G., Dresel, C., Gordon, M. L., Diehl‐Schmid, J., Grimmer, T., Yakushev, I. Y., Mattis, P. J., Ma, Y., et al. (2018) · Alzheimer s & Dementia Diagnosis Assessment & Disease Monitoring · doi:10.1016/j.dadm.2018.07.009 · Cited by 24 · see citing works
Li, Y., Cui, Z., Liao, Q., Dong, H., Zhang, J., Shen, W., & Zhou, W. (2019) · Addiction Biology · doi:10.1111/adb.12705 · Cited by 24 · see citing works
Amen, D., Darmal, B., Raji, C. A., Bao, W., Jorandby, L., Meysami, S., & Raghavendra, C. S. (2016) · Journal of Alzheimer s Disease · doi:10.3233/jad-160833 · Cited by 22 · see citing works
Anatomical Landmarks and DAG Network Learning for Alzheimer’s Disease Diagnosis
Tian, Z., Cao, C., Wang, Z., Xu, G., & Qiao, J. (2020) · IEEE Access · doi:10.1109/access.2020.3037107 · Cited by 21 · see citing works
Zhutovsky, P., Vijverberg, E. G. B., Bruin, W. B., Thomas, R. M., Wattjes, M. P., Pijnenburg, Y. A. L., van Wingen, G. A., & Dols, A. (2019) · Journal of Alzheimer's Disease · doi:10.3233/jad-181004 · Cited by 20 · see citing works
Self-citationLi, P., Quan, W., Wang, Z., Liu, Y., Cai, H., Chen, Y., Wang, Y., Zhang, M., Tian, Z., Zhang, H., et al. (2022) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2022.981451 · Cited by 19 · see citing works
Ishii, K. (2019) · Japanese Journal of Radiology · doi:10.1007/s11604-019-00881-9 · Cited by 19 · see citing works
Agostinho, D., Caramelo, F. J., Moreira, A. P., Santana, I. J., Abrunhosa, A., & Castelo-Branco, M. S. (2022) · Frontiers in Neuroscience · doi:10.3389/fnins.2021.638175 · Cited by 18 · see citing works
An update on magnetic resonance imaging markers in AD
Leocadi, M., Canu, E., Calderaro, D., Corbetta, D., Filippi, M., & Agosta, F. (2020) · Therapeutic Advances in Neurological Disorders · doi:10.1177/1756286420947986 · Cited by 17 · see citing works
Feis, R. A., Bouts, M. J. R. J., Panman, J. L., Jiskoot, L. C., Dopper, E. G. P., Schouten, T. M., de Vos, F., van der Grond, J., Swieten, J. C. V., & Rombouts, S. A. R. B. (2019) · NeuroImage Clinical · doi:10.1016/j.nicl.2019.101718 · Cited by 17 · see citing works
Self-citationVentricular features as reliable differentiators between bvFTD and other dementias
Manera, A. L., Dadar, M., Collins, D. L., & Ducharme, S. (2022) · NeuroImage Clinical · doi:10.1016/j.nicl.2022.102947 · Cited by 18 · see citing works
Borchert, R. J., Azevedo, T., Badhwar, A., Bernal, J., Betts, M. R., Bruffaerts, R., Burkhart, M. C., Dewachter, I., Gellersen, H. M., Low, A., et al. (2021) · medRxiv · doi:10.1101/2021.12.12.21267677 · Cited by 17 · see citing works
McKenna, M. C., Murad, A. A., Huynh, W., Lope, J., & Bede, P. (2022) · Expert Review of Neurotherapeutics · doi:10.1080/14737175.2022.2048648 · Cited by 16 · see citing works
Mirabian, S., Mohammadian, F., Ganji, Z., Zare, H., & Khalesi, E. H. (2025) · The Neuroradiology Journal · doi:10.1177/19714009251313511 · Cited by 15 · see citing works
Di Benedetto, M., Carrara, F., Tafuri, B., Nigro, S., De Blasi, R., Falchi, F., Gennaro, C., Gigli, G., Logroscino, G., & Amato, G. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.105937 · Cited by 14 · see citing works
McKenna, M. C., Tahedl, M., Murad, A. A., Lope, J., Hardiman, O. M., Hutchinson, S., & Bede, P. (2022) · Brain and Behavior · doi:10.1002/brb3.2500 · Cited by 13 · see citing works
Liu, F., Yuan, S., Li, W., Xu, Q., Wu, X. C., Han, K., Wang, J., & Miao, S. (2024) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2024.106398 · Cited by 14 · see citing works
Radiomics Model for Frontotemporal Dementia Diagnosis Using T1-Weighted MRI
Tafuri, B., Filardi, M., Urso, D., Blasi, R. D. D., Rizzo, G., Nigro, S., & Logroscino, G. (2022) · Frontiers in Neuroscience · doi:10.3389/fnins.2022.828029 · Cited by 13 · see citing works
Dattola, S., Ielo, A., Varone, G., Cacciola, A., Quartarone, A., & Bonanno, L. (2025) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2025.1547727 · Cited by 14 · see citing works
Tang, S., Wu, J., Xu, S., Li, Q., & He, J. (2023) · Frontiers in Genetics · doi:10.3389/fgene.2023.1071085 · Cited by 12 · see citing works
Kuhn, T. P., Becerra, S. A., Duncan, J., Spivak, N. M., Dang, B. H., Habelhah, B., Mahdavi, K. D., Mamoun, M. S., Whitney, M., Pereles, F. S., et al. (2021) · Quantitative Imaging in Medicine and Surgery · doi:10.21037/qims-20-1355 · Cited by 12 · see citing works
Interpretable Differential Diagnosis for Alzheimer’s Disease and Frontotemporal Dementia
Nguyen, H., Clément, M., Mansencal, B., & Pierrick Coupé, A. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-16431-6_6 · Cited by 12 · see citing works
Magnetic resonance imaging in dementia
Rodríguez, L. R., Salazar, D. J. T., García, N. F., Hernández, L. P., & Guinea, Ó. F. (2018) · Radiología (English Edition) · doi:10.1016/j.rxeng.2018.07.004 · Cited by 12 · see citing works
Pérez-Millan, A., Thirion, B., Falgàs, N., Borrego-Écija, S., Bosch, B., Juncà-Parella, J., Tort-Merino, A., Sarto, J., Augé, J. M., Antonell, A., et al. (2024) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2024.08.008 · Cited by 10 · see citing works
Advances in computerized MRI‐based biomarkers in Alzheimer’s disease
Wong, R., Luo, Y., Mok, V., & Shi, L. (2021) · Brain Science Advances · doi:10.26599/bsa.2021.9050005 · Cited by 10 · see citing works
Ho, S. Y.-C., Chien, T., Lin, M.-L., & Tsai, K. (2023) · Medicine · doi:10.1097/md.0000000000032670 · Cited by 10 · see citing works
Goyal, P., Rani, R., & Singh, K. (2021) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-021-09674-8 · Cited by 9 · see citing works
Evaluation of Feature Selection for Alzheimer’s Disease Diagnosis
Gu, F., Ma, S., Wang, X., Zhao, J., Yu, Y. K., & Song, X. (2022) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2022.924113 · Cited by 9 · see citing works
Gafuroğlu, C., Rekik, I., & In, A. T. A. D. N. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-00931-1_50 · Cited by 8 · see citing works
Deep learning-based classification of multi-categorical Alzheimer's disease data.
Cohen, D. S., Carpenter, K. A., Jarrell, J. T., & Huang, X. (2019) · PubMed · Cited by 8 · see citing works
Efficient Diagnosis of Alzheimer’s Disease Using EfficientNet in Neuroimaging
Sharen, H., Dhanush, B., Rukmani, P., & Dhanya, D. (2022) · Lecture notes in electrical engineering · doi:10.1007/978-981-19-2980-9_18 · Cited by 8 · see citing works
Zheng, W., Initiative, A. D. N., Cui, B., Sun, Z., Li, X., Han, X., Yu, Y., Li, K., Hu, L., & Wang, Z. (2020) · Aging · doi:10.18632/aging.103017 · Cited by 8 · see citing works
Krüger, J., Opfer, R., Spies, L., Hedderich, D. M., & Buchert, R. (2023) · European Radiology · doi:10.1007/s00330-023-10356-1 · Cited by 7 · see citing works
Resonancia magnética en las demencias
Rodríguez, L. R., Salazar, D. J. T., García, N. F., Hernández, L. P., & Guinea, Ó. F. (2018) · Radiología · doi:10.1016/j.rx.2018.04.003 · Cited by 5 · see citing works
Tafuri, B., Filardi, M., Frisullo, M. E., Blasi, R. D. D., Rizzo, G., Nigro, S., & Logroscino, G. (2023) · Brain and Behavior · doi:10.1002/brb3.2896 · Cited by 2 · see citing works
Computer-Aided Diagnosis and Prediction in Brain Disorders
Venkatraghavan, V., van der Voort, S. R., Bos, D., Smits, M., Barkhof, F., Niessen, W. J., Klein, S., & Bron, E. E. (2023) · Neuromethods · doi:10.1007/978-1-0716-3195-9_15 · Cited by 4 · see citing works
Self-citationSong, Z., Li, H., Zhang, Y., Zhu, C., Jiang, M., Song, L., Wang, Y., Ouyang, M., Hu, F., & Zheng, Q. (2024) · Magnetic Resonance Materials in Physics Biology and Medicine · doi:10.1007/s10334-024-01178-3 · Cited by 5 · see citing works
Regional Abnormality Representation Learning in Structural MRI for AD/MCI Diagnosis
Choi, J.-S., Lee, E., & Suk, H. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-00919-9_8 · Cited by 4 · see citing works
Vyas, A., Aisopos, F., Vidal, M.-E., Garrard, P., & Paliouras, G. (2021) · Research Square · doi:10.21203/rs.3.rs-336921/v1 · Cited by 4 · see citing works
Hybrid Model based on ANFIS with Gray Level Co-occurrence Matrix for Dementia Prediction
Kour, H., Sharma, R., Manhas, J., & Sharma, V. (2023) · Journal of Scientific Research · doi:10.3329/jsr.v15i2.61672 · Cited by 3 · see citing works
An Optimal Framework for Alzheimer’s Disease Diagnosis
Alsaraira, A., Alabed, S., Hamad, E. M., & Saraereh, O. A. (2023) · Intelligent Automation & Soft Computing · doi:10.32604/iasc.2023.036950 · Cited by 2 · see citing works
Gao, Z., Zhu, W., Li, Y., Ye, W., Cheng, X. J., Zhou, S., Li, X., Li, X., Yu, Y., & Initiative, T. A. D. N. (2024) · Journal of Alzheimer s Disease · doi:10.1177/13872877241296130 · Cited by 2 · see citing works
Amran, A. Y., Bukhbinder, A. S., & Schulz, P. E. (2022) · doi:10.1093/med/9780197574317.003.0018 · Cited by 1 · see citing works
Nguyen, H., Clément, M., Mansencal, B., & Pierrick Coupé, A. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-45676-3_6 · Cited by 1 · see citing works
Yang, X., & Guo, Y. (2018) · Translational bioinformatics · doi:10.1007/978-981-10-8764-6_13 · Cited by 1 · see citing works
Data-driven algorithm for the diagnosis of behavioral variant frontotemporal dementiaPreprint
Manera, A. L., Dadar, M., Swieten, J. C. V., Borroni, B., Sánchez‐Valle, R., Moreno, F., Laforce, R. J., Graff, C., Synofzik, M., Galimberti, D., et al. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2019.12.19.883462 · Cited by 1 · see citing works
Ventricle features as reliable differentiators between the bvFTD and other dementiasPreprint
Manera, A. L., Dadar, M., Collins, D. L., & Ducharme, S. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2103.03065 · Cited by 1 · see citing works
Research progress of psychoradiology in geriatric neurodegenerative diseases
Suo, X., Li, Z., Zuo, C., Lan, H., Pan, N., Lui, S., & Gong, Q. (2022) · Scientia Sinica Vitae · doi:10.1360/ssv-2022-0168
Augmenting radiological assessment of imaging evident dementias with radiomic analysis
Puducheri, S., Zhou, O. T., Kapadia, K., Romano, M. F., Yalamanchili, S., Agrawal, A., Arasa, V. C. A., Farris, C. W., Mian, A. Z., Paul, A. B., et al. (2025) · npj Dementia · doi:10.1038/s44400-025-00031-1
Microstructural grey matter alterations in patients with behavioural variant frontotemporal dementia
Akbarian, B., Hett, K., Phan, T. X., Lee, J. J., Eaton, J. E., Donahue, M. J., & Darby, R. R. (2025) · Brain Communications · doi:10.1093/braincomms/fcaf427
Vyas, A., Aisopos, F., Vidal, M.-E., Garrard, P., & Παλιούρας, Γ. (2022) · Figshare · doi:10.6084/m9.figshare.21351434
Vyas, A., Aisopos, F., Vidal, M.-E., Garrard, P., & Παλιούρας, Γ. (2022) · Figshare · doi:10.6084/m9.figshare.21351440
Yu, Q., Mai, Y., Ruan, Y., Luo, Y., Zhao, L., Fang, W., Cao, Z., Li, Y., Liao, W., Xiao, S., et al. (2021) · Figshare · doi:10.6084/m9.figshare.13566108
Vyas, A., Aisopos, F., Vidal, M.-E., Garrard, P., & Παλιούρας, Γ. (2022) · Figshare · doi:10.6084/m9.figshare.21351437
Yu, Q., Mai, Y., Ruan, Y., Luo, Y., Zhao, L., Fang, W., Cao, Z., Li, Y., Liao, W., Xiao, S., et al. (2021) · Figshare · doi:10.6084/m9.figshare.13566111
Vyas, A., Aisopos, F., Vidal, M.-E., Garrard, P., & Παλιούρας, Γ. (2022) · Figshare · doi:10.6084/m9.figshare.21351431
- Authors
- Möller, C., Pijnenburg, Y. A. L., van der Flier, W. M., Versteeg, A., Tijms, B., de Munck, J. C., Hafkemeijer, A., Rombouts, S. A. R. B., van der Grond, J., van Swieten, J., Dopper, E., Scheltens, P., Barkhof, F., Vrenken, H., & Wink, A. M.
- Affiliations
- Erasmus MC (NL)
- Leiden University (NL)
- ORCID
- Venue
- Radiology
- Published
- 2016-06
- Volume
- 279
- Issue
- 3
- Pages
- 838-848
- ISSN
- 0033-8419 (ISSN-L)
- 1527-1315
- OpenAlex
- W2192985207
- Cited by
- 103
- References
- 47
- Citations by year
- 2026: 3 · 2025: 4 · 2024: 5 · 2023: 15 · 2022: 22 · 2021: 14 · 2020: 8 · 2019: 12 · 2018: 15 · 2017: 2 · 2016: 3
- FWCI
- 5.43
- Type
- journal article
- Language
- en
- Open access
- green
- pure.amsterdamumc.nl
- Licence
- other-oa
- Locations
- Radiology (publishedVersion)
- PubMed (publishedVersion)
- Pure Amsterdam UMC (publishedVersion, other-oa)
- Amsterdam UMC (VU Amsterdam) - Institutional Repository (publishedVersion)
- EUR Research Repository (Erasmus University Rotterdam) (publishedVersion)
- UCL Discovery (University College London) (submittedVersion)
- EUR Research Repository (Erasmus University Rotterdam) (submittedVersion)
- Data Archiving and Networked Services (DANS) (submittedVersion)
- Leiden Repository (Leiden University) (submittedVersion)
- Funders
- Vanderbilt University Medical Center
- Vrije Universiteit Amsterdam
- Stichting Dioraphte
- Alzheimer Nederland
- Topics
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Health Sciences › Medicine › Physiology › Alzheimer's disease research and treatments
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- Alzheimer's disease, behavioral variant frontotemporal dementia, cortical atrophy, support vector machine, T1-weighted structural MRI, differential diagnosis
- MeSH terms
- Alzheimer Disease, Atrophy, Diagnosis, Differential, Female, Humans, Magnetic Resonance Imaging, Male, Middle Aged, Neuropsychological Tests, ROC Curve, Frontotemporal Dementia, Support Vector Machine, Gray Matter
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
