Diagnostic neuroimaging across diseases
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Fully automated classification algorithms have been successfully applied to diagnose a wide range of neurological and psychiatric diseases. They are sufficiently robust to handle data from different scanners for many applications and in specific cases outperform radiologists. This article provides an overview of current applications taking structural imaging in Alzheimer's disease and schizophrenia as well as functional imaging to diagnose depression as examples. In this context, we also report studies aiming to predict the future course of the disease and the response to treatment for the individual. This has obvious clinical relevance but is also important for the design of treatment studies that may aim to include a cohort with a predicted fast disease progression to be more sensitive to detect treatment effects. In the second part, we present our own opinions on i) the role these classification methods can play in the clinical setting; ii) where their limitations are at the moment and iii) how those can be overcome. Specifically, we discuss strategies to deal with disease heterogeneity, diagnostic uncertainties, a probabilistic framework for classification and multi-class classification approaches.
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Classifying participants using several measurements (e.g., fMRI data from few distinct VSWM tasks), where each measurement enables better than chance classification and there is substantial independency between measurements, enabled higher classification accuracies compared to using data from each single measurement.
“As expected based on this theorem, classifying participants by using several measurements for each participant (e.g., fMRI data from few distinct VSWM tasks), where each measurement enables a better than chance classification accuracy, and where there is a substantial degree of independency between the measurements (e.g., distinct cognitive tasks), enabled higher classification accuracies compared”
From Functional neuroimaging of visuospatial working memory tasks enables accurate detection of attention deficit and hyperactivity disorder (2015) · cites this paper as Klöppel et al., 2012
The improvement in classification accuracy when using PCs from four tasks together is consistent with the underlying theorem of ensemble-based classification.
“This is consistent with the underlying theorem of ensemble-based classification (Rokach, 2010; Klöppel et al., 2012).”
From Functional neuroimaging of visuospatial working memory tasks enables accurate detection of attention deficit and hyperactivity disorder (2015) · cites this paper as Klöppel et al., 2012
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Wu, Y., & Vapnik, V. N. (1999) · Technometrics · doi:10.2307/1271368 · Cited by 27,236 · 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-citationGaussian Processes for Machine Learning
Rasmussen, C. E., & Williams, C. K. I. (2005) · doi:10.7551/mitpress/3206.001.0001 · Cited by 11,705 · see citing works
Albert, M. S., DeKosky, S. T., Dickson, D., Dubois, B., Feldman, H. H., Fox, N. C., Gamst, A., Holtzman, D. M., Jagust, W. J., Petersen, R. C., et al. (2011) · Alzheimer's & Dementia · doi:10.1016/j.jalz.2011.03.008 · Cited by 10,152 · see citing works
An introduction to variable and feature selection
GuyonIsabelle, & ElisseeffAndré (2003) · Journal of Machine Learning Research · doi:10.5555/944919.944968 · Cited by 7,843 · see citing works
Sperling, R. A., Aisen, P. S., Beckett, L. A., Bennett, D. A., Craft, S., Fagan, A. M., Iwatsubo, T., Jack, C. R., Kaye, J., Montine, T. J., et al. (2011) · Alzheimer's & Dementia · doi:10.1016/j.jalz.2011.03.003 · Cited by 7,155 · see citing works
Self-citationKernel Methods for Pattern Analysis
Shawe-Taylor, J. S., & Cristianini, N. (2004) · Cambridge University Press eBooks · doi:10.1017/cbo9780511809682 · Cited by 6,610 · see citing works
An Introduction to Variable and Feature Selection.
Guyon, I., & Elisseeff, A. (2003) · Cited by 6,576 · see citing works
Current concepts in mild cognitive impairment
Petersen, R. C., Doody, R., Kurz, A., Mohs, R. C., Morris, J. C., Rabins, P. V., Ritchie, K., Rossor, M., Thal, L., & Winblad, B. (2001) · Archives of Neurology · doi:10.1001/archneur.58.12.1985 · Cited by 4,781 · see citing works
The Alzheimer’s disease neuroimaging initiative (ADNI): MRI methods
Jack, C. R., Bernstein, M. A., Fox, N. C., Thompson, P., Alexander, G., Harvey, D., Borowski, B., Britson, P. J., L. Whitwell, J., Ward, C., et al. (2008) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.21049 · Cited by 4,347 · see citing works
An introduction to kernel-based learning algorithms
Müller, K. R., Mika, S., Rätsch, G., Tsuda, K., & Schölkopf, B. (2001) · IEEE Transactions on Neural Networks · doi:10.1109/72.914517 · Cited by 3,485 · see citing works
Toward discovery science of human brain function
Biswal, B. B., Mennes, M., Zuo, X.-N., Gohel, S., Kelly, C., Smith, S. M., Beckmann, C. F., Adelstein, J. S., Buckner, R. L., Colcombe, S., et al. (2010) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0911855107 · Cited by 2,970 · see citing works
Ensemble based systems in decision making
Robi Polikar, A. (2006) · IEEE Circuits and Systems Magazine · doi:10.1109/mcas.2006.1688199 · Cited by 2,960 · see citing works
Albert, M., DeKosky, S. T., Dickson, D. W., Dubois, B., Feldman, H., Fox, N. C., Gamst, A. C., Holtzman, D. M., Jagust, W. J., Petersen, R. C., et al. (2013) · FOCUS The Journal of Lifelong Learning in Psychiatry · doi:10.1176/appi.focus.11.1.96 · Cited by 2,630 · see citing works
Marcus, D. S., Wang, T. H., Parker, J., Csernansky, J. G., Morris, J. C., & Buckner, R. L. (2007) · Journal of Cognitive Neuroscience · doi:10.1162/jocn.2007.19.9.1498 · Cited by 2,163 · see citing works
Jack, C. R., Albert, M. S., Knopman, D. S., McKhann, G. M., Sperling, R. A., Carrillo, M. C., Thies, B., & Phelps, C. H. (2011) · Alzheimer's & Dementia · doi:10.1016/j.jalz.2011.03.004 · Cited by 2,042 · see citing works
Self-citationRevising the definition of Alzheimer's disease: a new lexicon
Dubois, B., Feldman, H. H., Jacova, C., Cummings, J. L., DeKosky, S. T., Barberger-Gateau, P., Delacourte, A., Frisoni, G., Fox, N. C., Galasko, D., et al. (2010) · The Lancet Neurology · doi:10.1016/s1474-4422(10)70223-4 · Cited by 1,869 · see citing works
Unconscious determinants of free decisions in the human brain
Soon, C. S., Brass, M., Heinze, H.-J., & Haynes, J.-D. (2008) · Nature Neuroscience · doi:10.1038/nn.2112 · Cited by 1,741 · see citing works
The Alzheimer's Disease Neuroimaging Initiative
Mueller, S. G., Weiner, M. W., Thal, L. J., Petersen, R. C., Jack, C., Jagust, W., Trojanowski, J. Q., Toga, A. W., & Beckett, L. (2005) · Neuroimaging Clinics of North America · doi:10.1016/j.nic.2005.09.008 · Cited by 1,525 · see citing works
Self-citationMultimodal 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
Self-citationHonea, R., Crow, T. J., Passingham, D., & Mackay, C. E. (2005) · American Journal of Psychiatry · doi:10.1176/appi.ajp.162.12.2233 · Cited by 1,157 · see citing works
Long-term Antipsychotic Treatment and Brain Volumes
Ho, B., Andreasen, N. C., Ziebell, S., Pierson, R. K., & Magnotta, V. A. (2011) · Archives of General Psychiatry · doi:10.1001/archgenpsychiatry.2010.199 · Cited by 975 · see citing works
Cuingnet, R., Gérardin, E., Tessieras, J., Auzias, G., Lehéricy, S., Habert, M., Marie Chupin, A., Benali, H., & Olivier Colliot, A. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.06.013 · Cited by 962 · see citing works
Koolschijn, P. C. M. P., van Haren, N. E. M., Lensvelt‐Mulders, G. J. L. M., Hulshoff Pol, H. E., & Kahn, R. S. (2009) · Human Brain Mapping · doi:10.1002/hbm.20801 · Cited by 902 · see citing works
Sparse multinomial logistic regression: fast algorithms and generalization bounds
Krishnapuram, B., Carin, L., Figueiredo, M. A. T., & Hartemink, A. J. (2005) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2005.127 · Cited by 872 · see citing works
Franke, K., Ziegler, G., Klöppel, S., & Christian Gaser, B. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.01.005 · Cited by 799 · see citing works
Self-citationFu, C. H. Y., Williams, S. C. R., Cleare, A. J., Brammer, M. J., Walsh, N. D., Kim, J., Andrew, C. M., Pich, E. M., Williams, P. M., Reed, L. J., et al. (2004) · Archives of General Psychiatry · doi:10.1001/archpsyc.61.9.877 · Cited by 775 · see citing works
Introduction to machine learning for brain imaging
Lemm, S., Blankertz, B., Dickhaus, T., & Müller, K. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.11.004 · Cited by 626 · see citing works
Prediction of MCI to AD conversion, via MRI, CSF biomarkers, and pattern classification
Davatzikos, C. A., Bhatt, P., Shaw, L. M. J., Batmanghelich, K. N., & Trojanowski, J. Q. (2010) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2010.05.023 · Cited by 578 · see citing works
Berendsen, A. A. M., Kang, J. H., van de Rest, O., Jankovic, N., Kampman, E., Jong, J. C. K., Franco, O. H., Ikram, M. A., Pikhart, H., Nilsson, L. M., et al. (2017) · Dementia and Geriatric Cognitive Disorders · doi:10.1159/000464269 · Cited by 565 · see citing works
Alzheimer's disease diagnosis in individual subjects using structural MR images: Validation studies
Vemuri, P., Gunter, J. L., Senjem, M. L., Whitwell, J. L., Kantarci, K., Knopman, D. S., Boeve, B. F., Petersen, R. C., & Jack, C. R. (2007) · NeuroImage · doi:10.1016/j.neuroimage.2007.09.073 · Cited by 455 · see citing works
Self-citationMargin based feature selection - theory and algorithms
Gilad-Bachrach, R., Navot, A., & Tishby, N. (2004) · International Conference on Machine Learning · doi:10.1145/1015330.1015352 · Cited by 427 · see citing works
Koutsouleris, N., Meisenzahl, E. M., Davatzikos, C. A., Bottlender, R., Frodl, T., Scheuerecker, J., Schmitt, G., Zetzsche, T., Decker, P., Reiser, M. F., et al. (2009) · Archives of General Psychiatry · doi:10.1001/archgenpsychiatry.2009.62 · Cited by 400 · see citing works
Self-citationMRI and CSF biomarkers in normal, MCI, and AD subjects
Vemuri, P., Wiste, H. J., Weigand, S. D., Shaw, L. M. J., Trojanowski, J. Q., Weiner, M. W., Knopman, D. S., Petersen, R. C., & Jack, C. R. (2009) · Neurology · doi:10.1212/wnl.0b013e3181af79fb · Cited by 399 · see citing works
Self-citationDetection of prodromal Alzheimer's disease via pattern classification of magnetic resonance imaging
Davatzikos, C., Fan, Y., Wu, X., Shen, D., & Resnick, S. M. (2008) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2006.11.010 · Cited by 385 · see citing works
Medial temporal lobe atrophy on MRI predicts dementia in patients with mild cognitive impairment
Korf, E. S. C., Wahlund, L., Visser, P. J., & Scheltens, P. (2004) · Neurology · doi:10.1212/01.wnl.0000133114.92694.93 · Cited by 342 · see citing works
Automated 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
Self-citationFu, C. H. Y., Mourão-Miranda, J., Costafreda, S. G., Khanna, A., Marquand, A. F., Williams, S., & Brammer, M. J. (2007) · Biological Psychiatry · doi:10.1016/j.biopsych.2007.08.020 · Cited by 337 · see citing works
Self-citationDavatzikos, C., Xu, F., An, Y., Fan, Y., & Resnick, S. M. (2009) · Brain · doi:10.1093/brain/awp091 · Cited by 315 · see citing works
Lerch, J. P., Pruessner, J. C., Zijdenbos, A. P., Collins, D. L., Teipel, S. J., Hampel, H. J., & Evans, A. C. (2006) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2006.09.013 · Cited by 281 · see citing works
Walhovd, K. B., Fjell, A. M., Brewer, J., McEvoy, L. K., Fennema-Notestine, C., Hagler, D. J., Jennings, R. G., Karow, D., Dale, A. M., & Initiative, T. A. D. N. (2010) · American Journal of Neuroradiology · doi:10.3174/ajnr.a1809 · Cited by 279 · 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
Self-citationDavatzikos, C. A., Shen, D. G., Gur, R. C., Wu, X., Liu, D., Yong Fan, A., Hughett, P., Turetsky, B. I., & Gur, R. E. (2005) · Archives of General Psychiatry · doi:10.1001/archpsyc.62.11.1218 · Cited by 270 · see citing works
Sparse logistic regression for whole-brain classification of fMRI data
Ryali, S., Supekar, K., Abrams, D. A., & Menon, V. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.02.040 · Cited by 263 · see citing works
Predicting clinical scores from magnetic resonance scans in Alzheimer's disease
Stonnington, C. M., Chu, C., Klöppel, S., Jack, C. R., Ashburner, J., & Frackowiak, R. S. J. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.03.051 · Cited by 256 · see citing works
Self-citationMRI and CSF biomarkers in normal, MCI, and AD subjects
Vemuri, P., Wiste, H. J., Weigand, S. D., Shaw, L. M. J., Trojanowski, J. Q., Weiner, M. W., Knopman, D. S., Petersen, R. C., & Jack, C. R. (2009) · Neurology · doi:10.1212/wnl.0b013e3181af79e5 · Cited by 246 · see citing works
Self-citationIndividual 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 244 · see citing works
McKhann, G. M., Knopman, D. S., Chertkow, H. M., Hyman, B. T., Kawas, C. H., Klunk, W. E., Koroshetz, W. J., Manly, J. J., Mohs, R. C., Morris, J. C., et al. (2011) · Alzheimer s & Dementia · Cited by 240 · see citing works
Prognostic and diagnostic potential of the structural neuroanatomy of depression
Costafreda, S. G., Chu, C., Ashburner, J., & Fu, C. H. Y. (2009) · PLoS ONE · doi:10.1371/journal.pone.0006353 · Cited by 232 · see citing works
Marquand, A. F., Howard, M. A., Brammer, M. J., Chu, C., Coen, S. J., & Mourão-Miranda, J. (2009) · NeuroImage · doi:10.1016/j.neuroimage.2009.10.072 · Cited by 226 · see citing works
Self-citationDeCarli, C. S. (2007) · Archives of Neurology · doi:10.1001/archneur.64.1.108 · Cited by 208 · see citing works
Teipel, S. J., Born, C., Ewers, M., Bokde, A. L. W., Reiser, M. F., Möller, H., & Hampel, H. J. (2007) · NeuroImage · doi:10.1016/j.neuroimage.2007.07.008 · Cited by 200 · see citing works
Bayesian decoding of brain images
Friston, K. J., Chu, C., Mourão-Miranda, J., Hulme, O. J., Rees, G. E., Penny, W. D., & Ashburner, J. (2007) · NeuroImage · doi:10.1016/j.neuroimage.2007.08.013 · Cited by 196 · see citing works
Self-citationHeterogeneity of Schizophrenia
Tsuang, M. T., Lyons, M. J., & Faraone, S. V. (1990) · The British Journal of Psychiatry · doi:10.1192/bjp.156.1.17 · Cited by 194 · see citing works
Kawasaki, Y., Suzuki, M., Kherif, F., Takahashi, T., Zhou, S.-Y., Nakamura, K., Matsui, M., Sumiyoshi, T., Seto, H., & Kurachi, M. (2006) · NeuroImage · doi:10.1016/j.neuroimage.2006.08.018 · Cited by 180 · see citing works
Multivariate and univariate neuroimaging biomarkers of Alzheimer's disease
Habeck, C. G., Foster, N. L., Perneczky, R., Kurz, A., Alexopoulos, P., Koeppe, R. A., Drzezga, A. E., & Stern, Y. (2008) · NeuroImage · doi:10.1016/j.neuroimage.2008.01.056 · Cited by 173 · see citing works
Vemuri, P., Whitwell, J. L., Kantarci, K., Josephs, K. A., Parisi, J. E., Shiung, M. S., Knopman, D. S., Boeve, B. F., Petersen, R. C., Dickson, D. W., et al. (2008) · NeuroImage · doi:10.1016/j.neuroimage.2008.05.012 · Cited by 172 · see citing works
Self-citationNouretdinov, I., Costafreda, S. G., Gammerman, A., Chervonenkis, A. Y., Vovk, V., Vapnik, V. N., & Fu, C. H. Y. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.05.023 · Cited by 164 · see citing works
Characterizing Alzheimer's disease using a hypometabolic convergence index
Chen, K., Ayutyanont, N., Langbaum, J. B. S., Fleisher, A., Reschke, C., Lee, W., Liu, X., Bandy, D., Alexander, G. E., Thompson, P. M., et al. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.01.049 · Cited by 160 · 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
Sun, D., van Erp, T. G. M., Thompson, P. M., Bearden, C. E., Daley, M., Kushan, L., Hardt, M. E., Nuechterlein, K. H., Toga, A. W., & Cannon, T. D. (2009) · Biological Psychiatry · doi:10.1016/j.biopsych.2009.07.019 · Cited by 148 · see citing works
Integrating Neurobiological Markers of Depression
Hahn, T., Marquand, A. F., Ehlis, A., Dresler, T., Kittel‐Schneider, S., Jarczok, T. A., Lesch, K., Jakob, P. M., Mourão-Miranda, J., Brammer, M. J., et al. (2010) · Archives of General Psychiatry · doi:10.1001/archgenpsychiatry.2010.178 · Cited by 142 · see citing works
Self-citationMultivariate examination of brain abnormality using both structural and functional MRI
Yong Fan, A., Rao, H., Hurt, H., Giannetta, J. M., Korczykowski, M., Shera, D. M., Avants, B., Gee, J. C., Wang, J., & Shen, D. G. (2007) · NeuroImage · doi:10.1016/j.neuroimage.2007.04.009 · Cited by 135 · see citing works
Koutsouleris, N., Davatzikos, C. A., Ronald Bottlender, A., Patschurek‐Kliche, K., Scheuerecker, J., Decker, P., Christian Gaser, B., Möller, H., & Meisenzahl, E. M. (2011) · Schizophrenia Bulletin · doi:10.1093/schbul/sbr037 · Cited by 132 · see citing works
Self-citationMourão-Miranda, J., Hardoon, D. R., Hahn, T., Marquand, A. F., Williams, S. C. R., Shawe-Taylor, J., & Brammer, M. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.06.042 · Cited by 131 · see citing works
Self-citationCostafreda, S. G., Khanna, A., Mourao-Miranda, J., & Fu, C. H. Y. (2009) · NeuroReport · doi:10.1097/wnr.0b013e3283294159 · Cited by 129 · see citing works
Neuroanatomy of verbal working memory as a diagnostic biomarker for depression
Marquand, A. F., Mourão-Miranda, J., Brammer, M. J., Cleare, A. J., & Fu, C. H. Y. (2008) · Neuroreport · doi:10.1097/wnr.0b013e328310425e · Cited by 125 · see citing works
Self-citationAntemortem 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
Self-citationDiffusion tensor imaging reliably differentiates patients with schizophrenia from healthy volunteers
Ardekani, B. A., Tabesh, A., Sevy, S. M., Robinson, D. G., Bilder, R. M., & Szeszko, P. R. (2010) · Human Brain Mapping · doi:10.1002/hbm.20995 · Cited by 98 · see citing works
Aharoni, E., Funk, C. M., Sinnott-Armstrong, W. P., & Gazzaniga, M. S. (2008) · Annals of the New York Academy of Sciences · doi:10.1196/annals.1440.007 · Cited by 97 · see citing works
Nearest Neighbor Based Feature Selection for Regression and its Application to Neural Activity
Navot, A., Shpigelman, L., Tishby, N., et al. (2005) · Cited by 93 · see citing works
Automatic detection of preclinical neurodegeneration
Klöppel, S., Chu, C., Tan, G. C.-Y., Draganski, B., Johnson, H. J., Paulsen, J. S., Kienzle, W., Tabrizi, S. J., Ashburner, J., Frackowiak, R. S. J., et al. (2009) · Neurology · doi:10.1212/01.wnl.0000341768.28646.b6 · Cited by 92 · see citing works
Self-citationMachine-learning techniques for building a diagnostic model for very mild dementia
Chen, R., & Herskovits, E. H. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.03.084 · Cited by 67 · see citing works
Detecting concealed information using brain-imaging technology
Bles, M., & Haynes, J. (2008) · Neurocase · doi:10.1080/13554790801992784 · Cited by 60 · see citing works
Koutsouleris, N., Christian Gaser, B., Ronald Bottlender, A., Davatzikos, C. A., Decker, P., Jäger, M., Schmitt, G., Reiser, M. F., Möller, H., & Meisenzahl, E. M. (2010) · Schizophrenia Research · doi:10.1016/j.schres.2010.08.032 · Cited by 59 · see citing works
Self-citationClassification Based on Cortical Folding Patterns
Duchesnay, É., Cachia, A., Roche, A., Rivière, D., Cointepas, Y., Orfanos, D. P., Zilbovicius, M., Martinot, J., Régis, J. M., & Mangin, J.-F. (2007) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2007.892501 · Cited by 57 · see citing works
Semi-supervised cluster analysis of imaging data
Filipovych, R., Resnick, S. M., & Davatzikos, C. A. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.09.074 · Cited by 53 · see citing works
Teipel, S., Bokde, A. L. W., Born, C., Meindl, T., Reiser, M. F., Møller, H. J., & Hampel, H. J. (2007) · Brain · doi:10.1093/brain/awm117 · Cited by 52 · see citing works
Evaluating SVM and MLDA in the extraction of discriminant regions for mental state prediction
Sato, J. R., Fujita, A., Thomaz, C. E., et al. (2009) · NeuroImage · doi:10.1016/j.neuroimage.2009.01.032 · Cited by 52 · see citing works
Diagnosis of Brain Abnormality Using both Structural and Functional MR Images
Yong Fan, A., Rao, H., Giannetta, J. M., Hurt, H., Wang, J., Davatzikos, C. A., & Shen, D. G. (2006) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/iembs.2006.259260 · Cited by 24 · see citing works
A plea for confidence intervals and consideration of generalizability in diagnostic studies
Klöppel, S., Stonnington, C. M., Chu, C., et al. (2008) · Brain · doi:10.1093/brain/awn091 · Cited by 20 · see citing works
Self-citationKlöppel, S. (2009) · Current Opinion in Neurology · doi:10.1097/wco.0b013e328332ba0f · Cited by 14 · see citing works
Self-citationDiscrimination of AD and normal subjects from MRI: Anatomical versus statistical regions
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- Schölkopf B Smola A Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond (Adaptive Computation and Machine Learning) 2001 The MIT Press Cambridge, MA
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- Authors
- Klöppel, S., Abdulkadir, A., Jack, C. R., Koutsouleris, N., Mourão-Miranda, J., & Vemuri, P.
- Affiliations
- University Medical Center Freiburg (DE)
- Mayo Clinic (US)
- Ludwig-Maximilians-Universität München (DE)
- Corresponding author
- Klöppel, Stefan
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2012-06
- Volume
- 61
- Issue
- 2
- Pages
- 457-463
- ISSN
- 1053-8119 (ISSN-L)
- PMC
- PMC3420067
- OpenAlex
- W2028739995
- Cited by
- 279
- References
- 85
- Citations by year
- 2026: 5 · 2025: 14 · 2024: 11 · 2023: 9 · 2022: 17 · 2021: 11 · 2020: 13 · 2019: 21 · 2018: 27 · 2017: 20 · 2016: 43 · 2015: 30 · 2014: 27 · 2013: 24 · 2012: 7
- FWCI
- 2.43
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- NeuroImage (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion, cc-by-nc-nd)
- PubMed Central (acceptedVersion, cc-by-nc-nd)
- MPG.PuRe (Max Planck Society) (submittedVersion, other-oa)
- UCL Discovery (University College London) (submittedVersion)
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion)
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion, cc-by-nc-nd)
- Funders
- National Institute on Aging (R01AG011378)
- Topics
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- automatic classification, neuroimaging, Alzheimer's disease, schizophrenia, depression, disease progression prediction, treatment response prediction, disease heterogeneity, probabilistic classification, multi-class classification, structural MRI, functional MRI, clinical implementation of AI
- MeSH terms
- Alzheimer Disease, Mental Disorders, Dementia, Depressive Disorder, Humans, Image Processing, Computer-Assisted, Nervous System Diseases, Schizophrenia, Neuroimaging, Support Vector Machine
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.
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