Nichols, B.N., Nichols, T.E., Pellman, J., Poline, J.-B., Rokem, A., Schaefer, G., Sochat, V., Triplett, W., Turner, J.A., Varoquaux, G., Poldrack, R.A., 2016. The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments. Scientific Data 3, 160044. https://doi.org/10.1038/sdata.2016.44

Gousias, I.S., Rueckert, D., Heckemann, R.A., Dyet, L.E., Boardman, J.P., Edwards, A.D., Hammers, A., 2008. Automatic segmentation of brain MRIs of 2-year-olds into 83 regions of interest. Neuroimage 40, 672–684. https://doi.org/10.1016/j.neuroimage.2007.11.034

Gray, K.R., Aljabar, P., Heckemann, R.A., Hammers, A., Rueckert, D., 2013. Random forest-based similarity measures for multi-modal classification of Alzheimer's disease. NeuroImage 65, 167–175. https://doi.org/10.1016/j.neuroimage.2012.09.065

Guerrero, R., Wolz, R., Rao, A.W., Rueckert, D., 2014. Manifold population modeling as a neuro-imaging biomarker: Application to ADNI and ADNI-GO. NeuroImage 94, 275–286. https://doi.org/10.1016/j.neuroimage.2014.03.036

Haller, S., Lovblad, K.O., Giannakopoulos, P., 2011. Principles of Classification Analyses in Mild Cognitive Impairment (MCI) and Alzheimer Disease. Journal of Alzheimer's Disease 26, 389–394. https://doi.org/10.3233/JAD-2011-0014

Hammers, A., Allom, R., Koepp, M.J., Free, S.L., Myers, R., Lemieux, L., Mitchell, T.N., Brooks, D.J., Duncan, J.S., 2003. Three-dimensional maximum probability atlas of the human brain, with particular reference to the temporal lobe. Hum Brain Mapp 19, 224–247. https://doi.org/10.1002/hbm.10123

Hinrichs, C., Singh, V., Mukherjee, L., Xu, G., Chung, M.K., Johnson, S.C., Alzheimer's Disease Neuroimaging Initiative, 2009. Spatially augmented LPboosting for AD classification with evaluations on the ADNI dataset. Neuroimage 48, 138–149. https://doi.org/10.1016/j.neuroimage.2009.05.056

Holmes, C.J., Hoge, R., Collins, L., Woods, R., Toga, A.W., Evans, A.C., 1998. Enhancement of MR images using registration for signal averaging. J Comput Assist Tomogr 22, 324–333.

Jack, C.R., Albert, M.S., Knopman, D.S., McKhann, G.M., Sperling, R.A., Carrillo, M.C., Thies, B., Phelps, C.H., 2011. Introduction to the recommendations from the National Institute on Aging-Alzheimer's Association workgroups on diagnostic guidelines for Alzheimer's disease. Alzheimers Dement 7, 257–262. https://doi.org/10.1016/j.jalz.2011.03.004

Jack, C.R., Bernstein, M.A., Borowski, B.J., Gunter, J.L., Fox, N.C., Thompson, P.M., Schuff, N., Krueger, G., Killiany, R.J., DeCarli, C.S., Dale, A.M., Carmichael, O.W., Tosun, D., Weiner, M.W., 2010a. Update on the Magnetic Resonance Imaging core of the Alzheimer's Disease Neuroimaging Initiative. Alzheimer's & Dementia 6, 212–220. https://doi.org/10.1016/j.jalz.2010.03.004

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., Dale, A.M., Felmlee, J.P., Gunter, J.L., Hill, D.L.G., Killiany, R., Schuff, N., Fox-Bosetti, S., Lin, C., Studholme, C., DeCarli, C.S., Gunnar Krueger, Ward, H.A., Metzger, G.J., Scott, K.T., Mallozzi, R., Blezek, D., Levy, J., Debbins, J.P., Fleisher, A.S., Albert, M., Green, R., Bartzokis, G., Glover, G., Mugler, J., Weiner, M.W., 2008. The Alzheimer's disease neuroimaging initiative (ADNI): MRI methods. J. Magn. Reson. Imaging 27, 685–691. https://doi.org/10.1002/jmri.21049

Jack, C.R., Knopman, D.S., Jagust, W.J., Shaw, L.M., Aisen, P.S., Weiner, M.W., Petersen, R.C., Trojanowski, J.Q., 2010b. Hypothetical model of dynamic biomarkers of the Alzheimer's pathological cascade. Lancet Neurol 9, 119–128. https://doi.org/10.1016/S1474-4422(09)70299-6
Nichols, B.N., Nichols, T.E., Pellman, J., Poline, J.-B., Rokem, A., Schaefer, G., Sochat, V., Triplett, W., Turner, J.A., Varoquaux, G., Poldrack, R.A., 2016. The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments. Scientific Data 3, 160044. https://doi.org/10.1038/sdata.2016.44

Gousias, I.S., Rueckert, D., Heckemann, R.A., Dyet, L.E., Boardman, J.P., Edwards, A.D., Hammers, A., 2008. Automatic segmentation of brain MRIs of 2-year-olds into 83 regions of interest. Neuroimage 40, 672–684. https://doi.org/10.1016/j.neuroimage.2007.11.034

Gray, K.R., Aljabar, P., Heckemann, R.A., Hammers, A., Rueckert, D., 2013. Random forest-based similarity measures for multi-modal classification of Alzheimer's disease. NeuroImage 65, 167–175. https://doi.org/10.1016/j.neuroimage.2012.09.065

Guerrero, R., Wolz, R., Rao, A.W., Rueckert, D., 2014. Manifold population modeling as a neuro-imaging biomarker: Application to ADNI and ADNI-GO. NeuroImage 94, 275–286. https://doi.org/10.1016/j.neuroimage.2014.03.036

Haller, S., Lovblad, K.O., Giannakopoulos, P., 2011. Principles of Classification Analyses in Mild Cognitive Impairment (MCI) and Alzheimer Disease. Journal of Alzheimer's Disease 26, 389–394. https://doi.org/10.3233/JAD-2011-0014

Hammers, A., Allom, R., Koepp, M.J., Free, S.L., Myers, R., Lemieux, L., Mitchell, T.N., Brooks, D.J., Duncan, J.S., 2003. Three-dimensional maximum probability atlas of the human brain, with particular reference to the temporal lobe. Hum Brain Mapp 19, 224–247. https://doi.org/10.1002/hbm.10123

Hinrichs, C., Singh, V., Mukherjee, L., Xu, G., Chung, M.K., Johnson, S.C., Alzheimer's Disease Neuroimaging Initiative, 2009. Spatially augmented LPboosting for AD classification with evaluations on the ADNI dataset. Neuroimage 48, 138–149. https://doi.org/10.1016/j.neuroimage.2009.05.056

Holmes, C.J., Hoge, R., Collins, L., Woods, R., Toga, A.W., Evans, A.C., 1998. Enhancement of MR images using registration for signal averaging. J Comput Assist Tomogr 22, 324–333.

Jack, C.R., Albert, M.S., Knopman, D.S., McKhann, G.M., Sperling, R.A., Carrillo, M.C., Thies, B., Phelps, C.H., 2011. Introduction to the recommendations from the National Institute on Aging-Alzheimer's Association workgroups on diagnostic guidelines for Alzheimer's disease. Alzheimers Dement 7, 257–262. https://doi.org/10.1016/j.jalz.2011.03.004

Jack, C.R., Bernstein, M.A., Borowski, B.J., Gunter, J.L., Fox, N.C., Thompson, P.M., Schuff, N., Krueger, G., Killiany, R.J., DeCarli, C.S., Dale, A.M., Carmichael, O.W., Tosun, D., Weiner, M.W., 2010a. Update on the Magnetic Resonance Imaging core of the Alzheimer's Disease Neuroimaging Initiative. Alzheimer's & Dementia 6, 212–220. https://doi.org/10.1016/j.jalz.2010.03.004

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., Dale, A.M., Felmlee, J.P., Gunter, J.L., Hill, D.L.G., Killiany, R., Schuff, N., Fox-Bosetti, S., Lin, C., Studholme, C., DeCarli, C.S., Gunnar Krueger, Ward, H.A., Metzger, G.J., Scott, K.T., Mallozzi, R., Blezek, D., Levy, J., Debbins, J.P., Fleisher, A.S., Albert, M., Green, R., Bartzokis, G., Glover, G., Mugler, J., Weiner, M.W., 2008. The Alzheimer's disease neuroimaging initiative (ADNI): MRI methods. J. Magn. Reson. Imaging 27, 685–691. https://doi.org/10.1002/jmri.21049

Jack, C.R., Knopman, D.S., Jagust, W.J., Shaw, L.M., Aisen, P.S., Weiner, M.W., Petersen, R.C., Trojanowski, J.Q., 2010b. Hypothetical model of dynamic biomarkers of the Alzheimer's pathological cascade. Lancet Neurol 9, 119–128. https://doi.org/10.1016/S1474-4422(09)70299-6
Jagust, W.J., Bandy, D., Chen, K., Foster, N.L., Landau, S.M., Mathis, C.A., Price, J.C., Reiman, E.M., Skovronsky, D., Koeppe, R.A., 2010. The Alzheimer's Disease Neuroimaging Initiative positron emission tomography core. Alzheimer's & Dementia 6, 221-229. https://doi.org/10.1016/j.jalz.2010.03.003

Jagust, W.J., Landau, S.M., Koeppe, R.A., Reiman, E.M., Chen, K., Mathis, C.A., Price, J.C., Foster, N.L., Wang, A.Y., 2015. The Alzheimer's Disease Neuroimaging Initiative 2 PET Core: 2015. Alzheimer's & Dementia 11, 757-771. https://doi.org/10.1016/j.jalz.2015.05.001

Jie, B., Zhang, D., Cheng, B., Shen, D., the Alzheimer's Disease Neuroimaging Initiative, 2015. Manifold regularized multitask feature learning for multimodality disease classification. Hum. Brain Mapp. 36, 489-507. https://doi.org/10.1002/hbm.22642

Joliot, M., Jobard, G., Naveau, M., Delcroix, N., Petit, L., Zago, L., Crivello, F., Mellet, E., Mazoyer, B., Tzourio-Mazoyer, N., 2015. AICHA: An atlas of intrinsic connectivity of homotopic areas. J. Neurosci. Methods 254, 46-59. https://doi.org/10.1016/j.jneumeth.2015.07.013

Ke, N.R., Goyal, A., Lamb, A., Pineau, J., Bengio, S., Bengio, Y. (Eds.), 2017. Reproducibility in Machine Learning Research Workshop of the International Conference on Machine Learning, Sydney, Australia.

Klöppel, 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. Automatic classification of MR scans in Alzheimer's disease. Brain 131, 681-689. https://doi.org/10.1093/brain/awm319

Knopman, D.S., DeKosky, S.T., Cummings, J.L., Chui, H., Corey-Bloom, J., Relkin, N., Small, G.W., Miller, B., Stevens, J.C., 2001. Practice parameter: diagnosis of dementia (an evidence-based review). Report of the Quality Standards Subcommittee of the American Academy of Neurology. Neurology 56, 1143-1153.

Landau, S.M., Breault, C., Joshi, A.D., Pontecorvo, M., Mathis, C.A., Jagust, W.J., Mintun, M.A., 2013. Amyloid-β Imaging with Pittsburgh Compound B and Florbetapir: Comparing Radiotracers and Quantification Methods. J Nucl Med 54, 70-77. https://doi.org/10.2967/jnumed.112.109009

Li, X., Morgan, P.S., Ashburner, J., Smith, J., Rorden, C., 2016. The first step for neuroimaging data analysis: DICOM to NIfTI conversion. J. Neurosci. Methods 264, 47-56. https://doi.org/10.1016/j.jneumeth.2016.03.001

Liu, M., Zhang, D., Shen, D., 2012. Ensemble sparse classification of Alzheimer's disease. NeuroImage 60, 1106-1116. https://doi.org/10.1016/j.neuroimage.2012.01.055

Liu, M., Zhang, D., Shen, D., Alzheimer's Disease Neuroimaging Initiative, 2015. View-centralized multi-atlas classification for Alzheimer's disease diagnosis. Hum Brain Mapp 36, 1847-1865. https://doi.org/10.1002/hbm.22741

Liu, M., Zhang, J., Adeli, E., Shen, D., 2018. Landmark-based deep multi-instance learning for brain disease diagnosis. Med Image Anal 43, 157-168. https://doi.org/10.1016/j.media.2017.10.005

Lu, D., Popuri, K., Ding, G.W., Balachandar, R., Beg, M.F., Alzheimer's Disease Neuroimaging Initiative, 2018. Multimodal and Multiscale Deep Neural Networks for the Early Diagnosis of Alzheimer's Disease using structural MR and FDG-PET images. Sci Rep 8, 5697. https://doi.org/10.1038/s41598-018-22871-z

Maggipinto, T., Bellotti, R., Amoroso, N., Diacono, D., Donvito, G., Lella, E., Monaco, A., Scelsi, M.A., Tangaro, S., Initiative, A.D.N., 2017. DTI measurements for Alzheimer's classification. Phys. Med. Biol. 62, 2361. https://doi.org/10.1088/1361-6560/aa5dbe

Marcus, D.S., Wang, T.H., Parker, J., Csernansky, J.G., Morris, J.C., Buckner, R.L., 2007. Open Access
Jagust, W.J., Bandy, D., Chen, K., Foster, N.L., Landau, S.M., Mathis, C.A., Price, J.C., Reiman, E.M., Skovronsky, D., Koeppe, R.A., 2010. The Alzheimer's Disease Neuroimaging Initiative positron emission tomography core. Alzheimer's & Dementia 6, 221-229. https://doi.org/10.1016/j.jalz.2010.03.003

Jagust, W.J., Landau, S.M., Koeppe, R.A., Reiman, E.M., Chen, K., Mathis, C.A., Price, J.C., Foster, N.L., Wang, A.Y., 2015. The Alzheimer's Disease Neuroimaging Initiative 2 PET Core: 2015. Alzheimer's & Dementia 11, 757-771. https://doi.org/10.1016/j.jalz.2015.05.001

Jie, B., Zhang, D., Cheng, B., Shen, D., the Alzheimer's Disease Neuroimaging Initiative, 2015. Manifold regularized multitask feature learning for multimodality disease classification. Hum. Brain Mapp. 36, 489-507. https://doi.org/10.1002/hbm.22642

Joliot, M., Jobard, G., Naveau, M., Delcroix, N., Petit, L., Zago, L., Crivello, F., Mellet, E., Mazoyer, B., Tzourio-Mazoyer, N., 2015. AICHA: An atlas of intrinsic connectivity of homotopic areas. J. Neurosci. Methods 254, 46-59. https://doi.org/10.1016/j.jneumeth.2015.07.013

Ke, N.R., Goyal, A., Lamb, A., Pineau, J., Bengio, S., Bengio, Y. (Eds.), 2017. Reproducibility in Machine Learning Research Workshop of the International Conference on Machine Learning, Sydney, Australia.

Klöppel, 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. Automatic classification of MR scans in Alzheimer's disease. Brain 131, 681-689. https://doi.org/10.1093/brain/awm319

Knopman, D.S., DeKosky, S.T., Cummings, J.L., Chui, H., Corey-Bloom, J., Relkin, N., Small, G.W., Miller, B., Stevens, J.C., 2001. Practice parameter: diagnosis of dementia (an evidence-based review). Report of the Quality Standards Subcommittee of the American Academy of Neurology. Neurology 56, 1143-1153.

Landau, S.M., Breault, C., Joshi, A.D., Pontecorvo, M., Mathis, C.A., Jagust, W.J., Mintun, M.A., 2013. Amyloid-β Imaging with Pittsburgh Compound B and Florbetapir: Comparing Radiotracers and Quantification Methods. J Nucl Med 54, 70-77. https://doi.org/10.2967/jnumed.112.109009

Li, X., Morgan, P.S., Ashburner, J., Smith, J., Rorden, C., 2016. The first step for neuroimaging data analysis: DICOM to NIfTI conversion. J. Neurosci. Methods 264, 47-56. https://doi.org/10.1016/j.jneumeth.2016.03.001

Liu, M., Zhang, D., Shen, D., 2012. Ensemble sparse classification of Alzheimer's disease. NeuroImage 60, 1106-1116. https://doi.org/10.1016/j.neuroimage.2012.01.055

Liu, M., Zhang, D., Shen, D., Alzheimer's Disease Neuroimaging Initiative, 2015. View-centralized multi-atlas classification for Alzheimer's disease diagnosis. Hum Brain Mapp 36, 1847-1865. https://doi.org/10.1002/hbm.22741

Liu, M., Zhang, J., Adeli, E., Shen, D., 2018. Landmark-based deep multi-instance learning for brain disease diagnosis. Med Image Anal 43, 157-168. https://doi.org/10.1016/j.media.2017.10.005

Lu, D., Popuri, K., Ding, G.W., Balachandar, R., Beg, M.F., Alzheimer's Disease Neuroimaging Initiative, 2018. Multimodal and Multiscale Deep Neural Networks for the Early Diagnosis of Alzheimer's Disease using structural MR and FDG-PET images. Sci Rep 8, 5697. https://doi.org/10.1038/s41598-018-22871-z

Maggipinto, T., Bellotti, R., Amoroso, N., Diacono, D., Donvito, G., Lella, E., Monaco, A., Scelsi, M.A., Tangaro, S., Initiative, A.D.N., 2017. DTI measurements for Alzheimer's classification. Phys. Med. Biol. 62, 2361. https://doi.org/10.1088/1361-6560/aa5dbe

Marcus, D.S., Wang, T.H., Parker, J., Csernansky, J.G., Morris, J.C., Buckner, R.L., 2007. Open Access
Series of Imaging Studies (OASIS): Cross-sectional MRI Data in Young, Middle Aged, Nondemented, and Demented Older Adults. Journal of Cognitive Neuroscience 19, 1498–1507.
https://doi.org/10.1162/jocn.2007.19.9.1498

McKhann, G., Drachman, D., Folstein, M., Katzman, R., Price, D., Stadlan, E.M., 1984. Clinical diagnosis of Alzheimer's disease: report of the NINCDS-ADRDA Work Group under the auspices of Department of Health and Human Services Task Force on Alzheimer's Disease. Neurology 34, 939–944.

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., Mohs, R.C., Morris, J.C., Rossor, M.N., Scheltens, P., Carrillo, M.C., Thies, B., Weintraub, S., Phelps, C.H., 2011. The diagnosis of dementia due to Alzheimer's disease: recommendations from the National Institute on Aging-Alzheimer's Association workgroups on diagnostic guidelines for Alzheimer's disease. Alzheimers Dement 7, 263–269.
https://doi.org/10.1016/j.jalz.2011.03.005

Moradi, E., Pepe, A., Gaser, C., Huttunen, H., Tohka, J., Alzheimer's Disease Neuroimaging Initiative, 2015. Machine learning framework for early MRI-based Alzheimer's conversion prediction in MCI subjects. Neuroimage 104, 398–412. https://doi.org/10.1016/j.neuroimage.2014.10.002

Nadeau, C., Bengio, Y., 2003. Inference for the Generalization Error. Machine Learning 52, 239–281.
https://doi.org/10.1023/A:1024068626366

Ota, K., Oishi, N., Ito, K., Fukuyama, H., SEAD-J Study Group, 2014. A comparison of three brain atlases for MCI prediction. J. Neurosci. Methods 221, 139–150. https://doi.org/10.1016/j.jneumeth.2013.10.003

Ota, K., Oishi, N., Ito, K., Fukuyama, H., SEAD-J Study Group, Alzheimer's Disease Neuroimaging Initiative, 2015. Effects of imaging modalities, brain atlases and feature selection on prediction of Alzheimer's disease. J. Neurosci. Methods 256, 168–183. https://doi.org/10.1016/j.jneumeth.2015.08.020

Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., Duchesnay, É., 2011. Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research 12, 2825–2830.

Petersen, R.C., Aisen, P.S., Beckett, L.A., Donohue, M.C., Gamst, A.C., Harvey, D.J., Jack, C.R., Jagust, W.J., Shaw, L.M., Toga, A.W., Trojanowski, J.Q., Weiner, M.W., 2010. Alzheimer's Disease Neuroimaging Initiative (ADNI): clinical characterization. Neurology 74, 201–209.
https://doi.org/10.1212/WNL.0b013e3181cb3e25

Poldrack, R.A., Baker, C.I., Durnez, J., Gorgolewski, K.J., Matthews, P.M., Munafò, M.R., Nichols, T.E., Poline, J.-B., Vul, E., Yarkoni, T., 2017. Scanning the horizon: towards transparent and reproducible neuroimaging research. Nat. Rev. Neurosci. 18, 115–126. https://doi.org/10.1038/nrn.2016.167

Querbes, O., Aubry, F., Pariente, J., Lotterie, J.-A., Démonet, J.-F., Duret, V., Puel, M., Berry, I., Fort, J.-C., Celsis, P., Alzheimer's Disease Neuroimaging Initiative, 2009. Early diagnosis of Alzheimer's disease using cortical thickness: impact of cognitive reserve. Brain 132, 2036–2047.
https://doi.org/10.1093/brain/awp105

Raamana, P.R., 2017. Neuropredict: Easy Machine Learning And Standardized Predictive Analysis Of Biomarkers. https://doi.org/10.5281/zenodo.1058993

Raamana, P.R., Strother, S.C., 2017. Impact of spatial scale and edge weight on predictive power of cortical thickness networks. bioRxiv. https://doi.org/10.1101/170381

Rathore, S., Habes, M., Iftikhar, M.A., Shacklett, A., Davatzikos, C., 2017. A review on neuroimaging-
Series of Imaging Studies (OASIS): Cross-sectional MRI Data in Young, Middle Aged, Nondemented, and Demented Older Adults. Journal of Cognitive Neuroscience 19, 1498–1507.
https://doi.org/10.1162/jocn.2007.19.9.1498

McKhann, G., Drachman, D., Folstein, M., Katzman, R., Price, D., Stadlan, E.M., 1984. Clinical diagnosis of Alzheimer's disease: report of the NINCDS-ADRDA Work Group under the auspices of Department of Health and Human Services Task Force on Alzheimer's Disease. Neurology 34, 939–944.

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., Mohs, R.C., Morris, J.C., Rossor, M.N., Scheltens, P., Carrillo, M.C., Thies, B., Weintraub, S., Phelps, C.H., 2011. The diagnosis of dementia due to Alzheimer's disease: recommendations from the National Institute on Aging-Alzheimer's Association workgroups on diagnostic guidelines for Alzheimer's disease. Alzheimers Dement 7, 263–269.
https://doi.org/10.1016/j.jalz.2011.03.005

Moradi, E., Pepe, A., Gaser, C., Huttunen, H., Tohka, J., Alzheimer's Disease Neuroimaging Initiative, 2015. Machine learning framework for early MRI-based Alzheimer's conversion prediction in MCI subjects. Neuroimage 104, 398–412. https://doi.org/10.1016/j.neuroimage.2014.10.002

Nadeau, C., Bengio, Y., 2003. Inference for the Generalization Error. Machine Learning 52, 239–281.
https://doi.org/10.1023/A:1024068626366

Ota, K., Oishi, N., Ito, K., Fukuyama, H., SEAD-J Study Group, 2014. A comparison of three brain atlases for MCI prediction. J. Neurosci. Methods 221, 139–150. https://doi.org/10.1016/j.jneumeth.2013.10.003

Ota, K., Oishi, N., Ito, K., Fukuyama, H., SEAD-J Study Group, Alzheimer's Disease Neuroimaging Initiative, 2015. Effects of imaging modalities, brain atlases and feature selection on prediction of Alzheimer's disease. J. Neurosci. Methods 256, 168–183. https://doi.org/10.1016/j.jneumeth.2015.08.020

Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., Duchesnay, É., 2011. Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research 12, 2825–2830.

Petersen, R.C., Aisen, P.S., Beckett, L.A., Donohue, M.C., Gamst, A.C., Harvey, D.J., Jack, C.R., Jagust, W.J., Shaw, L.M., Toga, A.W., Trojanowski, J.Q., Weiner, M.W., 2010. Alzheimer's Disease Neuroimaging Initiative (ADNI): clinical characterization. Neurology 74, 201–209.
https://doi.org/10.1212/WNL.0b013e3181cb3e25

Poldrack, R.A., Baker, C.I., Durnez, J., Gorgolewski, K.J., Matthews, P.M., Munafò, M.R., Nichols, T.E., Poline, J.-B., Vul, E., Yarkoni, T., 2017. Scanning the horizon: towards transparent and reproducible neuroimaging research. Nat. Rev. Neurosci. 18, 115–126. https://doi.org/10.1038/nrn.2016.167

Querbes, O., Aubry, F., Pariente, J., Lotterie, J.-A., Démonet, J.-F., Duret, V., Puel, M., Berry, I., Fort, J.-C., Celsis, P., Alzheimer's Disease Neuroimaging Initiative, 2009. Early diagnosis of Alzheimer's disease using cortical thickness: impact of cognitive reserve. Brain 132, 2036–2047.
https://doi.org/10.1093/brain/awp105

Raamana, P.R., 2017. Neuropredict: Easy Machine Learning And Standardized Predictive Analysis Of Biomarkers. https://doi.org/10.5281/zenodo.1058993

Raamana, P.R., Strother, S.C., 2017. Impact of spatial scale and edge weight on predictive power of cortical thickness networks. bioRxiv. https://doi.org/10.1101/170381

Rathore, S., Habes, M., Iftikhar, M.A., Shacklett, A., Davatzikos, C., 2017. A review on neuroimaging-
based classification studies and associated feature extraction methods for Alzheimer's disease and its prodromal stages. *NeuroImage* 155, 530-548. https://doi.org/10.1016/j.neuroimage.2017.03.057

Routier, A., Guillon, J., Burgos, N., Samper-Gonzalez, J., Wen, J., Fontanella, S., Bottani, S., Jacquemont, T., Marcoux, A., Gori, P., Lu, P., Moreau, T., Bacci, M., Durrleman, S., Colliot, O., 2018. Clinica: an open source software platform for reproducible clinical neuroscience studies. Presented at the Annual meeting of the Organization for Human Brain Mapping - OHBM 2018.

Sabuncu, M.R., Konukoglu, E., Initiative, for the A.D.N., 2015. Clinical Prediction from Structural Brain MRI Scans: A Large-Scale Empirical Study. *Neuroinform* 13, 31-46. https://doi.org/10.1007/s12021-014-9238-1

Salvatore, C., Cerasa, A., Battista, P., Gilardi, M.C., Quattrone, A., Castiglioni, I., 2015. Magnetic resonance imaging biomarkers for the early diagnosis of Alzheimer's disease: a machine learning approach. *Front. Neurosci.* 9. https://doi.org/10.3389/fnins.2015.00307

Schwarz, C.G., Gunter, J.L., Wiste, H.J., Przybelski, S.A., Weigand, S.D., Ward, C.P., Senjem, M.L., Vemuri, P., Murray, M.E., Dickson, D.W., Parisi, J.E., Kantarci, K., Weiner, M.W., Petersen, R.C., Jack, C.R., Alzheimer's Disease Neuroimaging Initiative, 2016. A large-scale comparison of cortical thickness and volume methods for measuring Alzheimer's disease severity. *Neuroimage* Clin 11, 802-812. https://doi.org/10.1016/j.nicl.2016.05.017

Shattuck, D.W., Mirza, M., Adisetiyo, V., Hojatkashani, C., Salamon, G., Narr, K.L., Poldrack, R.A., Bilder, R.M., Toga, A.W., 2008. Construction of a 3D probabilistic atlas of human cortical structures. *Neuroimage* 39, 1064-1080. https://doi.org/10.1016/j.neuroimage.2007.09.031

Sørensen, L., Nielsen, M., Alzheimer's Disease Neuroimaging Initiative, 2018. Ensemble support vector machine classification of dementia using structural MRI and mini-mental state examination. *J. Neurosci. Methods* 302, 66-74. https://doi.org/10.1016/j.jneumeth.2018.01.003

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., Park, D.C., Reiman, E.M., Rowe, C.C., Siemens, E., Stern, Y., Yaffe, K., Carrillo, M.C., Thies, B., Morrison-Bogorad, M., Wagster, M.V., Phelps, C.H., 2011. Toward defining the preclinical stages of Alzheimer's disease: recommendations from the National Institute on Aging-Alzheimer's Association workgroups on diagnostic guidelines for Alzheimer's disease. *Alzheimers Dement* 7, 280-292. https://doi.org/10.1016/j.jalz.2011.03.003

Suk, H.-I., Lee, S.-W., Shen, D., Alzheimer's Disease Neuroimaging Initiative, 2017. Deep ensemble learning of sparse regression models for brain disease diagnosis. *Med Image Anal* 37, 101-113. https://doi.org/10.1016/j.media.2017.01.008

Teipel, S.J., Kurth, J., Krause, B., Grothe, M.J., 2015. The relative importance of imaging markers for the prediction of Alzheimer's disease dementia in mild cognitive impairment — Beyond classical regression. *NeuroImage: Clinical* 8, 583-593. https://doi.org/10.1016/j.nicl.2015.05.006

Thomas, B.A., Cuplov, V., Bousse, A., Mendes, A., Thielemans, K., Hutton, B.F., Erlandsson, K., 2016. PETPVC: a toolbox for performing partial volume correction techniques in positron emission tomography. *Phys Med Biol* 61, 7975-7993. https://doi.org/10.1088/0031-9155/61/22/7975

Thomas, B.A., Erlandsson, K., Modat, M., Thurfjell, L., Vandenberghe, R., Ourselin, S., Hutton, B.F., 2011. The importance of appropriate partial volume correction for PET quantification in Alzheimer's disease. *Eur. J. Nucl. Med. Mol. Imaging* 38, 1104-1119. https://doi.org/10.1007/s00259-011-1745-9

Tohka, J., Moradi, E., Huttunen, H., Alzheimer's Disease Neuroimaging Initiative, 2016. Comparison of
based classification studies and associated feature extraction methods for Alzheimer's disease and its prodromal stages. *NeuroImage* 155, 530-548. https://doi.org/10.1016/j.neuroimage.2017.03.057

Routier, A., Guillon, J., Burgos, N., Samper-Gonzalez, J., Wen, J., Fontanella, S., Bottani, S., Jacquemont, T., Marcoux, A., Gori, P., Lu, P., Moreau, T., Bacci, M., Durrleman, S., Colliot, O., 2018. Clinica: an open source software platform for reproducible clinical neuroscience studies. Presented at the Annual meeting of the Organization for Human Brain Mapping - OHBM 2018.

Sabuncu, M.R., Konukoglu, E., Initiative, for the A.D.N., 2015. Clinical Prediction from Structural Brain MRI Scans: A Large-Scale Empirical Study. *Neuroinform* 13, 31-46. https://doi.org/10.1007/s12021-014-9238-1

Salvatore, C., Cerasa, A., Battista, P., Gilardi, M.C., Quattrone, A., Castiglioni, I., 2015. Magnetic resonance imaging biomarkers for the early diagnosis of Alzheimer's disease: a machine learning approach. *Front. Neurosci.* 9. https://doi.org/10.3389/fnins.2015.00307

Schwarz, C.G., Gunter, J.L., Wiste, H.J., Przybelski, S.A., Weigand, S.D., Ward, C.P., Senjem, M.L., Vemuri, P., Murray, M.E., Dickson, D.W., Parisi, J.E., Kantarci, K., Weiner, M.W., Petersen, R.C., Jack, C.R., Alzheimer's Disease Neuroimaging Initiative, 2016. A large-scale comparison of cortical thickness and volume methods for measuring Alzheimer's disease severity. *Neuroimage* Clin 11, 802-812. https://doi.org/10.1016/j.nicl.2016.05.017

Shattuck, D.W., Mirza, M., Adisetiyo, V., Hojatkashani, C., Salamon, G., Narr, K.L., Poldrack, R.A., Bilder, R.M., Toga, A.W., 2008. Construction of a 3D probabilistic atlas of human cortical structures. *Neuroimage* 39, 1064-1080. https://doi.org/10.1016/j.neuroimage.2007.09.031

Sørensen, L., Nielsen, M., Alzheimer's Disease Neuroimaging Initiative, 2018. Ensemble support vector machine classification of dementia using structural MRI and mini-mental state examination. *J. Neurosci. Methods* 302, 66-74. https://doi.org/10.1016/j.jneumeth.2018.01.003

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., Park, D.C., Reiman, E.M., Rowe, C.C., Siemens, E., Stern, Y., Yaffe, K., Carrillo, M.C., Thies, B., Morrison-Bogorad, M., Wagster, M.V., Phelps, C.H., 2011. Toward defining the preclinical stages of Alzheimer's disease: recommendations from the National Institute on Aging-Alzheimer's Association workgroups on diagnostic guidelines for Alzheimer's disease. *Alzheimers Dement* 7, 280-292. https://doi.org/10.1016/j.jalz.2011.03.003

Suk, H.-I., Lee, S.-W., Shen, D., Alzheimer's Disease Neuroimaging Initiative, 2017. Deep ensemble learning of sparse regression models for brain disease diagnosis. *Med Image Anal* 37, 101-113. https://doi.org/10.1016/j.media.2017.01.008

Teipel, S.J., Kurth, J., Krause, B., Grothe, M.J., 2015. The relative importance of imaging markers for the prediction of Alzheimer's disease dementia in mild cognitive impairment — Beyond classical regression. *NeuroImage: Clinical* 8, 583-593. https://doi.org/10.1016/j.nicl.2015.05.006

Thomas, B.A., Cuplov, V., Bousse, A., Mendes, A., Thielemans, K., Hutton, B.F., Erlandsson, K., 2016. PETPVC: a toolbox for performing partial volume correction techniques in positron emission tomography. *Phys Med Biol* 61, 7975-7993. https://doi.org/10.1088/0031-9155/61/22/7975

Thomas, B.A., Erlandsson, K., Modat, M., Thurfjell, L., Vandenberghe, R., Ourselin, S., Hutton, B.F., 2011. The importance of appropriate partial volume correction for PET quantification in Alzheimer's disease. *Eur. J. Nucl. Med. Mol. Imaging* 38, 1104-1119. https://doi.org/10.1007/s00259-011-1745-9

Tohka, J., Moradi, E., Huttunen, H., Alzheimer's Disease Neuroimaging Initiative, 2016. Comparison of
Feature Selection Techniques in Machine Learning for Anatomical Brain MRI in Dementia.
Neuroinformatics 14, 279-296. https://doi.org/10.1007/s12021-015-9292-3

Tong, T., Wolz, R., Gao, Q., Guerrero, R., Hajnal, J.V., Rueckert, D., 2014. Multiple instance learning for classification of dementia in brain MRI. Medical Image Analysis 18, 808-818. https://doi.org/10.1016/j.media.2014.04.006

Tzourio-Mazoyer, N., Landeau, B., Papathanassiou, D., Crivello, F., Etard, O., Delcroix, N., Mazoyer, B., Joliot, M., 2002. Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage 15, 273-289. https://doi.org/10.1006/nimg.2001.0978

Varoquaux, G., Raamana, P.R., Engemann, D.A., Hoyos-Idrobo, A., Schwartz, Y., Thirion, B., 2017. Assessing and tuning brain decoders: Cross-validation, caveats, and guidelines. Neuroimage 145, 166-179. https://doi.org/10.1016/j.neuroimage.2016.10.038

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., 2008. Alzheimer's disease diagnosis in individual subjects using structural MR images: validation studies. Neuroimage 39, 1186-1197. https://doi.org/10.1016/j.neuroimage.2007.09.073

Voevodskaya, O., Simmons, A., Nordenskjöld, R., Kullberg, J., Ahlström, H., Lind, L., Wahlund, L.-O., Larsson, E.-M., Westman, E., Alzheimer's Disease Neuroimaging Initiative, 2014. The effects of intracranial volume adjustment approaches on multiple regional MRI volumes in healthy aging and Alzheimer's disease. Front Aging Neurosci 6, 264. https://doi.org/10.3389/fnagi.2014.00264

Westman, E., Aguilar, C., Muehlboeck, J.-S., Simmons, A., 2013. Regional magnetic resonance imaging measures for multivariate analysis in Alzheimer's disease and mild cognitive impairment. Brain Topogr 26, 9-23. https://doi.org/10.1007/s10548-012-0246-x

Winblad, B., Palmer, K., Kivipelto, M., Jelic, V., Fratiglioni, L., Wahlund, L.-O., Nordberg, A., Bäckman, L., Albert, M., Almkvist, O., Arai, H., Basun, H., Blennow, K., de Leon, M., DeCarli, C., Erkinjuntti, T., Giacobini, E., Graff, C., Hardy, J., Jack, C., Jorm, A., Ritchie, K., van Duijn, C., Visser, P., Petersen, R.C., 2004. Mild cognitive impairment--beyond controversies, towards a consensus: report of the International Working Group on Mild Cognitive Impairment. J. Intern. Med. 256, 240-246. https://doi.org/10.1111/j.1365-2796.2004.01380.x

Wolz, R., Julkunen, V., Koikkalainen, J., Niskanen, E., Zhang, D.P., Rueckert, D., Soininen, H., Lötjönen, J., Alzheimer's Disease Neuroimaging Initiative, 2011. Multi-method analysis of MRI images in early diagnostics of Alzheimer's disease. PLoS ONE 6, e25446. https://doi.org/10.1371/journal.pone.0025446

Young, J., Modat, M., Cardoso, M.J., Mendelson, A., Cash, D., Ourselin, S., 2013. Accurate multimodal probabilistic prediction of conversion to Alzheimer's disease in patients with mild cognitive impairment. NeuroImage: Clinical 2, 735-745. https://doi.org/10.1016/j.nicl.2013.05.004

Yun, H.J., Kwak, K., Lee, J.-M., Initiative, A.D.N., 2015. Multimodal Discrimination of Alzheimer's Disease Based on Regional Cortical Atrophy and Hypometabolism. PLOS ONE 10, e0129250. https://doi.org/10.1371/journal.pone.0129250

Zhang, D., Wang, Y., Zhou, L., Yuan, H., Shen, D., 2011. Multimodal classification of Alzheimer's disease and mild cognitive impairment. NeuroImage 55, 856-867. https://doi.org/10.1016/j.neuroimage.2011.01.008

Zhu, X., Suk, H.-I., Shen, D., 2014. A novel matrix-similarity based loss function for joint regression and classification in AD diagnosis. Neuroimage 100, 91-105.
Feature Selection Techniques in Machine Learning for Anatomical Brain MRI in Dementia.
Neuroinformatics 14, 279-296. https://doi.org/10.1007/s12021-015-9292-3

Tong, T., Wolz, R., Gao, Q., Guerrero, R., Hajnal, J.V., Rueckert, D., 2014. Multiple instance learning for classification of dementia in brain MRI. Medical Image Analysis 18, 808-818. https://doi.org/10.1016/j.media.2014.04.006

Tzourio-Mazoyer, N., Landeau, B., Papathanassiou, D., Crivello, F., Etard, O., Delcroix, N., Mazoyer, B., Joliot, M., 2002. Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage 15, 273-289. https://doi.org/10.1006/nimg.2001.0978

Varoquaux, G., Raamana, P.R., Engemann, D.A., Hoyos-Idrobo, A., Schwartz, Y., Thirion, B., 2017. Assessing and tuning brain decoders: Cross-validation, caveats, and guidelines. Neuroimage 145, 166-179. https://doi.org/10.1016/j.neuroimage.2016.10.038

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., 2008. Alzheimer's disease diagnosis in individual subjects using structural MR images: validation studies. Neuroimage 39, 1186-1197. https://doi.org/10.1016/j.neuroimage.2007.09.073

Voevodskaya, O., Simmons, A., Nordenskjöld, R., Kullberg, J., Ahlström, H., Lind, L., Wahlund, L.-O., Larsson, E.-M., Westman, E., Alzheimer's Disease Neuroimaging Initiative, 2014. The effects of intracranial volume adjustment approaches on multiple regional MRI volumes in healthy aging and Alzheimer's disease. Front Aging Neurosci 6, 264. https://doi.org/10.3389/fnagi.2014.00264

Westman, E., Aguilar, C., Muehlboeck, J.-S., Simmons, A., 2013. Regional magnetic resonance imaging measures for multivariate analysis in Alzheimer's disease and mild cognitive impairment. Brain Topogr 26, 9-23. https://doi.org/10.1007/s10548-012-0246-x

Winblad, B., Palmer, K., Kivipelto, M., Jelic, V., Fratiglioni, L., Wahlund, L.-O., Nordberg, A., Bäckman, L., Albert, M., Almkvist, O., Arai, H., Basun, H., Blennow, K., de Leon, M., DeCarli, C., Erkinjuntti, T., Giacobini, E., Graff, C., Hardy, J., Jack, C., Jorm, A., Ritchie, K., van Duijn, C., Visser, P., Petersen, R.C., 2004. Mild cognitive impairment--beyond controversies, towards a consensus: report of the International Working Group on Mild Cognitive Impairment. J. Intern. Med. 256, 240-246. https://doi.org/10.1111/j.1365-2796.2004.01380.x

Wolz, R., Julkunen, V., Koikkalainen, J., Niskanen, E., Zhang, D.P., Rueckert, D., Soininen, H., Lötjönen, J., Alzheimer's Disease Neuroimaging Initiative, 2011. Multi-method analysis of MRI images in early diagnostics of Alzheimer's disease. PLoS ONE 6, e25446. https://doi.org/10.1371/journal.pone.0025446

Young, J., Modat, M., Cardoso, M.J., Mendelson, A., Cash, D., Ourselin, S., 2013. Accurate multimodal probabilistic prediction of conversion to Alzheimer's disease in patients with mild cognitive impairment. NeuroImage: Clinical 2, 735-745. https://doi.org/10.1016/j.nicl.2013.05.004

Yun, H.J., Kwak, K., Lee, J.-M., Initiative, A.D.N., 2015. Multimodal Discrimination of Alzheimer's Disease Based on Regional Cortical Atrophy and Hypometabolism. PLOS ONE 10, e0129250. https://doi.org/10.1371/journal.pone.0129250

Zhang, D., Wang, Y., Zhou, L., Yuan, H., Shen, D., 2011. Multimodal classification of Alzheimer's disease and mild cognitive impairment. NeuroImage 55, 856-867. https://doi.org/10.1016/j.neuroimage.2011.01.008

Zhu, X., Suk, H.-I., Shen, D., 2014. A novel matrix-similarity based loss function for joint regression and classification in AD diagnosis. Neuroimage 100, 91-105.