publication . Preprint . 2018

TADPOLE Challenge: Prediction of Longitudinal Evolution in Alzheimer's Disease

Marinescu, Razvan V.; Oxtoby, Neil P.; Young, Alexandra L.; Bron, Esther E.; Toga, Arthur W.; Weiner, Michael W.; Barkhof, Frederik; Fox, Nick C.; Klein, Stefan; Alexander, Daniel C.; ...
Open Access English
  • Published: 10 May 2018
Abstract
Comment: For more details on TADPOLE Challenge, see https://tadpole.grand-challenge.org/ This paper outlines the design of the TADPOLE Challenge. Paper contains 8 pages, 2 figures, 5 tables
Subjects
free text keywords: Quantitative Biology - Populations and Evolution, Statistics - Applications
Funded by
EC| EuroPOND
Project
EuroPOND
Data-driven models for Progression Of Neurological Disease
  • Funder: European Commission (EC)
  • Project Code: 666992
  • Funding stream: H2020 | RIA
,
NIH| Alzheimers Disease Neuroimaging Initiative
Project
  • Funder: National Institutes of Health (NIH)
  • Project Code: 1U01AG024904-01
  • Funding stream: NATIONAL INSTITUTE ON AGING
,
RCUK| EPSRC Centre for Doctoral Training in Medical Imaging
Project
  • Funder: Research Council UK (RCUK)
  • Project Code: EP/L016478/1
  • Funding stream: EPSRC
,
EC| AMYPAD
Project
AMYPAD
Amyloid imaging to Prevent Alzheimer’s Disease – Sofia ref.: 115952
  • Funder: European Commission (EC)
  • Project Code: 115952
  • Funding stream: H2020 | IMI2-RIA
Download from
38 references, page 1 of 3

Aisen, P. S., Petersen, R. C., Donohue, M. C., Gamst, A., Raman, R., Thomas, R. G., Walter, S., Trojanowski, J. Q., Shaw, L. M., Beckett, L. A., et al., 2010. Clinical core of the Alzheimer's Disease Neuroimaging Initiative: progress and plans. Alzheimer's & dementia: the journal of the Alzheimer's Association 6 (3), 239-246.

Allen, G. I., Amoroso, N., Anghel, C., Balagurusamy, V., Bare, C. J., Beaton, D., Bellotti, R., Bennett, D. A., Boehme, K. L., Boutros, P. C., et al., 2016. Crowdsourced estimation of cognitive decline and resilience in Alzheimer's disease. Alzheimer's & dementia: the journal of the Alzheimer's Association 12 (6), 645-653.

Ashburner, J., 2009. Computational anatomy with the SPM software. Magnetic resonance imaging 27 (8), 1163-1174. [OpenAIRE]

Bilgel, M., Prince, J. L., Wong, D. F., Resnick, S. M., Jedynak, B. M., 2016. A multivariate nonlinear mixed e ects model for longitudinal image analysis: Application to amyloid imaging. Neuroimage 134, 658-670.

Brodersen, K. H., Ong, C. S., Stephan, K. E., Buhmann, J. M., 2010. The balanced accuracy and its posterior distribution. In: Pattern recognition (ICPR), 2010 20th international conference on. IEEE, pp. 3121-3124.

Bron, E. E., Smits, M., Van Der Flier, W. M., Vrenken, H., Barkhof, F., Scheltens, P., Papma, J. M., Steketee, R. M., Orellana, C. M., Meijboom, R., et al., 2015. Standardized evaluation of algorithms for computer-aided diagnosis of dementia based on structural MRI: the CADDementia challenge. NeuroImage 111, 562-579. [OpenAIRE]

Donohue, M. C., Jacqmin-Gadda, H., Le Go , M., Thomas, R. G., Raman, R., Gamst, A. C., Beckett, L. A., Jack, C. R., Weiner, M. W., Dartigues, J.-F., et al., 2014. Estimating long-term multivariate progression from short-term data. Alzheimer's & dementia: the journal of the Alzheimer's Association 10 (5), S400-S410.

Doody, R. S., Pavlik, V., Massman, P., Rountree, S., Darby, E., Chan, W., 2010. Predicting progression of alzheimer's disease. Alzheimer's research & therapy 2 (1), 2. [OpenAIRE]

Durrleman, S., Pennec, X., Trouve´, A., Braga, J., Gerig, G., Ayache, N., 2013. Toward a comprehensive framework for the spatiotemporal statistical analysis of longitudinal shape data. International journal of computer vision 103 (1), 22-59. [OpenAIRE]

Fonteijn, H. M., Modat, M., Clarkson, M. J., Barnes, J., Lehmann, M., Hobbs, N. Z., Scahill, R. I., Tabrizi, S. J., Ourselin, S., Fox, N. C., Alexander, D. C., 2012. An event-based model for disease progression and its application in familial Alzheimer's disease and Huntington's disease. NeuroImage 60 (3), 1880-1889. [OpenAIRE]

Frisoni, G. B., Fox, N. C., Jack Jr, C. R., Scheltens, P., Thompson, P. M., 2010. The clinical use of structural MRI in Alzheimer disease. Nature Reviews Neurology 6 (2), 67.

Guerrero, R., Schmidt-Richberg, A., Ledig, C., Tong, T., Wolz, R., Rueckert, D., ADNI, et al., 2016. Instantiated mixed e ects modeling of Alzheimer's disease markers. NeuroImage 142, 113-125. [OpenAIRE]

Hand, D. J., Till, R. J., 2001. A simple generalisation of the area under the ROC curve for multiple class classification problems. Machine learning 45 (2), 171-186.

Iturria-Medina, Y., Sotero, R. C., Toussaint, P. J., Mateos-Pe´rez, J. M., Evans, A. C., Weiner, M. W., Aisen, P., Petersen, R., Jack, C. R., Jagust, W., et al., 2016. Early role of vascular dysregulation on late-onset Alzheimers disease based on multifactorial data-driven analysis. Nature communications 7, 11934. [OpenAIRE]

Jack Jr, C. R., Knopman, D. S., Jagust, W. J., Petersen, R. C., Weiner, M. W., Aisen, P. S., Shaw, L. M., Vemuri, P., Wiste, H. J., Weigand, S. D., et al., 2013. Update on hypothetical model of Alzheimers disease biomarkers. Lancet neurology 12 (2), 207.

38 references, page 1 of 3
Abstract
Comment: For more details on TADPOLE Challenge, see https://tadpole.grand-challenge.org/ This paper outlines the design of the TADPOLE Challenge. Paper contains 8 pages, 2 figures, 5 tables
Subjects
free text keywords: Quantitative Biology - Populations and Evolution, Statistics - Applications
Funded by
EC| EuroPOND
Project
EuroPOND
Data-driven models for Progression Of Neurological Disease
  • Funder: European Commission (EC)
  • Project Code: 666992
  • Funding stream: H2020 | RIA
,
NIH| Alzheimers Disease Neuroimaging Initiative
Project
  • Funder: National Institutes of Health (NIH)
  • Project Code: 1U01AG024904-01
  • Funding stream: NATIONAL INSTITUTE ON AGING
,
RCUK| EPSRC Centre for Doctoral Training in Medical Imaging
Project
  • Funder: Research Council UK (RCUK)
  • Project Code: EP/L016478/1
  • Funding stream: EPSRC
,
EC| AMYPAD
Project
AMYPAD
Amyloid imaging to Prevent Alzheimer’s Disease – Sofia ref.: 115952
  • Funder: European Commission (EC)
  • Project Code: 115952
  • Funding stream: H2020 | IMI2-RIA
Download from
38 references, page 1 of 3

Aisen, P. S., Petersen, R. C., Donohue, M. C., Gamst, A., Raman, R., Thomas, R. G., Walter, S., Trojanowski, J. Q., Shaw, L. M., Beckett, L. A., et al., 2010. Clinical core of the Alzheimer's Disease Neuroimaging Initiative: progress and plans. Alzheimer's & dementia: the journal of the Alzheimer's Association 6 (3), 239-246.

Allen, G. I., Amoroso, N., Anghel, C., Balagurusamy, V., Bare, C. J., Beaton, D., Bellotti, R., Bennett, D. A., Boehme, K. L., Boutros, P. C., et al., 2016. Crowdsourced estimation of cognitive decline and resilience in Alzheimer's disease. Alzheimer's & dementia: the journal of the Alzheimer's Association 12 (6), 645-653.

Ashburner, J., 2009. Computational anatomy with the SPM software. Magnetic resonance imaging 27 (8), 1163-1174. [OpenAIRE]

Bilgel, M., Prince, J. L., Wong, D. F., Resnick, S. M., Jedynak, B. M., 2016. A multivariate nonlinear mixed e ects model for longitudinal image analysis: Application to amyloid imaging. Neuroimage 134, 658-670.

Brodersen, K. H., Ong, C. S., Stephan, K. E., Buhmann, J. M., 2010. The balanced accuracy and its posterior distribution. In: Pattern recognition (ICPR), 2010 20th international conference on. IEEE, pp. 3121-3124.

Bron, E. E., Smits, M., Van Der Flier, W. M., Vrenken, H., Barkhof, F., Scheltens, P., Papma, J. M., Steketee, R. M., Orellana, C. M., Meijboom, R., et al., 2015. Standardized evaluation of algorithms for computer-aided diagnosis of dementia based on structural MRI: the CADDementia challenge. NeuroImage 111, 562-579. [OpenAIRE]

Donohue, M. C., Jacqmin-Gadda, H., Le Go , M., Thomas, R. G., Raman, R., Gamst, A. C., Beckett, L. A., Jack, C. R., Weiner, M. W., Dartigues, J.-F., et al., 2014. Estimating long-term multivariate progression from short-term data. Alzheimer's & dementia: the journal of the Alzheimer's Association 10 (5), S400-S410.

Doody, R. S., Pavlik, V., Massman, P., Rountree, S., Darby, E., Chan, W., 2010. Predicting progression of alzheimer's disease. Alzheimer's research & therapy 2 (1), 2. [OpenAIRE]

Durrleman, S., Pennec, X., Trouve´, A., Braga, J., Gerig, G., Ayache, N., 2013. Toward a comprehensive framework for the spatiotemporal statistical analysis of longitudinal shape data. International journal of computer vision 103 (1), 22-59. [OpenAIRE]

Fonteijn, H. M., Modat, M., Clarkson, M. J., Barnes, J., Lehmann, M., Hobbs, N. Z., Scahill, R. I., Tabrizi, S. J., Ourselin, S., Fox, N. C., Alexander, D. C., 2012. An event-based model for disease progression and its application in familial Alzheimer's disease and Huntington's disease. NeuroImage 60 (3), 1880-1889. [OpenAIRE]

Frisoni, G. B., Fox, N. C., Jack Jr, C. R., Scheltens, P., Thompson, P. M., 2010. The clinical use of structural MRI in Alzheimer disease. Nature Reviews Neurology 6 (2), 67.

Guerrero, R., Schmidt-Richberg, A., Ledig, C., Tong, T., Wolz, R., Rueckert, D., ADNI, et al., 2016. Instantiated mixed e ects modeling of Alzheimer's disease markers. NeuroImage 142, 113-125. [OpenAIRE]

Hand, D. J., Till, R. J., 2001. A simple generalisation of the area under the ROC curve for multiple class classification problems. Machine learning 45 (2), 171-186.

Iturria-Medina, Y., Sotero, R. C., Toussaint, P. J., Mateos-Pe´rez, J. M., Evans, A. C., Weiner, M. W., Aisen, P., Petersen, R., Jack, C. R., Jagust, W., et al., 2016. Early role of vascular dysregulation on late-onset Alzheimers disease based on multifactorial data-driven analysis. Nature communications 7, 11934. [OpenAIRE]

Jack Jr, C. R., Knopman, D. S., Jagust, W. J., Petersen, R. C., Weiner, M. W., Aisen, P. S., Shaw, L. M., Vemuri, P., Wiste, H. J., Weigand, S. D., et al., 2013. Update on hypothetical model of Alzheimers disease biomarkers. Lancet neurology 12 (2), 207.

38 references, page 1 of 3
Powered by OpenAIRE Research Graph
Any information missing or wrong?Report an Issue