Predicting progression of mild cognitive impairment to dementia using neuropsychological data: a supervised learning approach using time windows

go back to reference Noorbakhsh F, Overall CM, Power C. Deciphering complex mechanisms in neurodegenerative diseases: the advent of systems biology. Trends Neurosci. 2009;32(2):88–100.CrossRefPubMed Noorbakhsh F, Overall CM, Power C. Deciphering complex mechanisms in neurodegenerative diseases: the advent of systems biology. Trends Neurosci. 2009;32(2):88–100.CrossRefPubMed

go back to reference Prince M, Wimo A, Guerchet M, Gemma-Claire A, Wu Y-T, Prina M. World Alzheimer Report 2015: the global impact of dementia - an analysis of prevalence, incidence, cost and trends. London: Alzheimer’s Disease International (ADI); 2015. https://www.alz.co.uk/research/WorldAlzheimerReport2015.pdf. Prince M, Wimo A, Guerchet M, Gemma-Claire A, Wu Y-T, Prina M. World Alzheimer Report 2015: the global impact of dementia - an analysis of prevalence, incidence, cost and trends. London: Alzheimer’s Disease International (ADI); 2015. https://​www.​alz.​co.​uk/​research/​WorldAlzheimerRe​port2015.​pdf.

go back to reference Belleville S, Fouquet C, Duchesne S, Collins DL, Hudon C. Detecting early preclinical Alzheimer’s disease via cognition, neuropsychiatry, and neuroimaging: qualitative review and recommendations for testing. J Alzheimers Dis. 2014;42:S375–82.PubMed Belleville S, Fouquet C, Duchesne S, Collins DL, Hudon C. Detecting early preclinical Alzheimer’s disease via cognition, neuropsychiatry, and neuroimaging: qualitative review and recommendations for testing. J Alzheimers Dis. 2014;42:S375–82.PubMed

go back to reference Tay L, Lim W, Chan M, Ali N, Mahanum S, Chew P, Lim J, Chong M. New DSM-V neurocognitive disorders criteria and their impact on diagnostic classifications of mild cognitive impairment and dementia in a memory clinic setting. Am J Geriatr Psychiatry. 2015;23(8):768–79.CrossRefPubMed Tay L, Lim W, Chan M, Ali N, Mahanum S, Chew P, Lim J, Chong M. New DSM-V neurocognitive disorders criteria and their impact on diagnostic classifications of mild cognitive impairment and dementia in a memory clinic setting. Am J Geriatr Psychiatry. 2015;23(8):768–79.CrossRefPubMed

go back to reference Bondi MW, Edmonds EC, Jak AJ, Clark LR, Delano-Wood L, McDonald CR, Nation DA, Libon DJ, Au R, Galasko D, Salmon DP. Neuropsychological criteria for mild cognitive impairment improves diagnostic precision, biomarker associations, and progression rates. J Alzheimers Dis. 2014;42(1):275–89.PubMedPubMedCentral Bondi MW, Edmonds EC, Jak AJ, Clark LR, Delano-Wood L, McDonald CR, Nation DA, Libon DJ, Au R, Galasko D, Salmon DP. Neuropsychological criteria for mild cognitive impairment improves diagnostic precision, biomarker associations, and progression rates. J Alzheimers Dis. 2014;42(1):275–89.PubMedPubMedCentral

go back to reference Albert MS, DeKosky ST, Dickson D, Dubois B, Feldman HH, Fox NC, Gamst A, Holtzman DM, Jagust WJ, Petersen RC, Snyder PJ, Carrillo MC, Thies B, Phelps CH. The diagnosis of mild cognitive impairment due to Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimer’s Dementia. 2011;7(3):270–9.CrossRefPubMedPubMedCentral Albert MS, DeKosky ST, Dickson D, Dubois B, Feldman HH, Fox NC, Gamst A, Holtzman DM, Jagust WJ, Petersen RC, Snyder PJ, Carrillo MC, Thies B, Phelps CH. The diagnosis of mild cognitive impairment due to Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimer’s Dementia. 2011;7(3):270–9.CrossRefPubMedPubMedCentral

go back to reference Morris JC, Storandt M, Miller JP, McKeel DW, Price JL, Rubin EH, Berg L. Mild cognitive impairment represents early-stage Alzheimer disease. Arch Neurol. 2001;58(3):397–405.CrossRefPubMed Morris JC, Storandt M, Miller JP, McKeel DW, Price JL, Rubin EH, Berg L. Mild cognitive impairment represents early-stage Alzheimer disease. Arch Neurol. 2001;58(3):397–405.CrossRefPubMed

go back to reference Lee SJ, Ritchie CS, Yaffe K, Cenzer IS, Barnes DE. A clinical index to predict progression from mild cognitive impairment to dementia due to Alzheimer’s disease. PLoS One. 2014;9(12):e113535.CrossRefPubMedPubMedCentral Lee SJ, Ritchie CS, Yaffe K, Cenzer IS, Barnes DE. A clinical index to predict progression from mild cognitive impairment to dementia due to Alzheimer’s disease. PLoS One. 2014;9(12):e113535.CrossRefPubMedPubMedCentral

go back to reference Barnes DE, Cenzer IS, Yaffe K, Ritchie CS, Lee SJ. A point-based tool to predict conversion from mild cognitive impairment to probable Alzheimer’s disease. Alzheimers Dement. 2014;10(6):646–55.CrossRefPubMedPubMedCentral Barnes DE, Cenzer IS, Yaffe K, Ritchie CS, Lee SJ. A point-based tool to predict conversion from mild cognitive impairment to probable Alzheimer’s disease. Alzheimers Dement. 2014;10(6):646–55.CrossRefPubMedPubMedCentral

go back to reference Dickerson BC, Sperling RA, Hyman BT, Albert MS, Blacker D. Clinical prediction of Alzheimer disease dementia across the spectrum of mild cognitive impairment. Arch Gen Psychiatry. 2007;64(12):1443–50.CrossRefPubMedPubMedCentral Dickerson BC, Sperling RA, Hyman BT, Albert MS, Blacker D. Clinical prediction of Alzheimer disease dementia across the spectrum of mild cognitive impairment. Arch Gen Psychiatry. 2007;64(12):1443–50.CrossRefPubMedPubMedCentral

go back to reference Tabert MH, Manly JJ, Liu X, Pelton GH, Rosenblum S, Jacobs M, Zamora D, Goodkind M, Bell K, Stern Y, Devanand DP. Neuropsychological prediction of conversion to Alzheimer disease in patients with mild cognitive impairment. Arch Gen Psychiatry. 2006;63(8):916–24.CrossRefPubMed Tabert MH, Manly JJ, Liu X, Pelton GH, Rosenblum S, Jacobs M, Zamora D, Goodkind M, Bell K, Stern Y, Devanand DP. Neuropsychological prediction of conversion to Alzheimer disease in patients with mild cognitive impairment. Arch Gen Psychiatry. 2006;63(8):916–24.CrossRefPubMed

go back to reference Langa K, Levine D. The diagnosis and management of mild cognitive impairment: a clinical review. JAMA. 2014;312(23):2551–61.CrossRefPubMedPubMedCentral Langa K, Levine D. The diagnosis and management of mild cognitive impairment: a clinical review. JAMA. 2014;312(23):2551–61.CrossRefPubMedPubMedCentral

go back to reference Kruczyk M, Zetterberg H, Hansson O, Rolstad S, Minthon L, Wallin A, Blennow K, Komorowski J, Andersson M. Monte Carlo feature selection and rule-based models to predict Alzheimer’s disease in mild cognitive impairment. J Neural Transm. 2012;119:821–31.CrossRefPubMed Kruczyk M, Zetterberg H, Hansson O, Rolstad S, Minthon L, Wallin A, Blennow K, Komorowski J, Andersson M. Monte Carlo feature selection and rule-based models to predict Alzheimer’s disease in mild cognitive impairment. J Neural Transm. 2012;119:821–31.CrossRefPubMed

go back to reference Palmqvist S, Hertze J, Minthon L, Wattmo C, Zetterberg H, Blennow K, Londos E, Hansson O. Comparison of brief cognitive tests and CSF biomarkers in predicting Alzheimer’s disease in mild cognitive impairment: six-year follow-up study. PLoS One. 2012;7(6):e38639.CrossRefPubMedPubMedCentral Palmqvist S, Hertze J, Minthon L, Wattmo C, Zetterberg H, Blennow K, Londos E, Hansson O. Comparison of brief cognitive tests and CSF biomarkers in predicting Alzheimer’s disease in mild cognitive impairment: six-year follow-up study. PLoS One. 2012;7(6):e38639.CrossRefPubMedPubMedCentral

go back to reference Moradi E, Pepe A, Gaser C, Huttunen H, Tohka J. Machine learning framework for early MRI-based Alzheimer’s conversion prediction in MCI subjects. NeuroImage. 2014;104:398–412.CrossRefPubMed Moradi E, Pepe A, Gaser C, Huttunen H, Tohka J. Machine learning framework for early MRI-based Alzheimer’s conversion prediction in MCI subjects. NeuroImage. 2014;104:398–412.CrossRefPubMed

go back to reference Doyle OM, Westman E, Marquand AF, Mecocci P, Vellas B, Tsolaki M, Kłoszewska I, Soininen H, Lovestone S, Williams SCR, Simmons A. Predicting progression of Alzheimer’s disease using ordinal regression. PLoS One. 2014;9(8):e105542.CrossRefPubMedPubMedCentral Doyle OM, Westman E, Marquand AF, Mecocci P, Vellas B, Tsolaki M, Kłoszewska I, Soininen H, Lovestone S, Williams SCR, Simmons A. Predicting progression of Alzheimer’s disease using ordinal regression. PLoS One. 2014;9(8):e105542.CrossRefPubMedPubMedCentral

go back to reference Ayton S, Faux NG, Bush AI, Weiner MW, Aisen P, Petersen R, Jack CR Jr, Jagust W, Trojanowki JQ, Toga AW, Beckett L, Green RC, Saykin AJ, Morris J, Shaw LM, Khachaturian Z, Sorensen G, Kuller L, Raichle M, Paul S, Davies P, Fillit H, Hefti F, Holtzman D, Marcel Mesulam M, Potter W, Snyder P, Schwartz A, Montine T, Thomas RG, Donohue M, Walter S, Gessert D, Sather T, Jiminez G, Harvey D, Bernstein M, Fox N, Thompson P, Schuff N, Borowski B, Gunter J, Senjem M, Vemuri P, Jones D, Kantarci K, Ward C, Koeppe RA, Foster N, Reiman EM, Chen K, Mathis C, Landau S, Cairns NJ, Householder E, Taylor-Reinwald L, Lee V, Korecka M, Figurski M, Crawford K, Neu S, Foroud TM, Potkin S, Shen L, Faber K, Kim S, Nho K, Thal L, Buckholtz N, Albert M, Frank R, Hsiao J, Kaye J, Quinn J, Lind B, Carter R, Dolen S, Schneider LS, Pawluczyk S, Beccera M, Teodoro L, Spann BM, Brewer J, Vanderswag H, Fleisher A, Heidebrink JL, Lord JL, Mason SS, Albers CS, Knopman D, Johnson K, Doody RS, Villanueva-Meyer J, Chowdhury M, Rountree S, Dang M, Stern Y, Honig LS, Bell KL, Ances B, Carroll M, Leon S, Mintun MA, Schneider S, Oliver A, Marson D, Griffith R, Clark D, Geldmacher D, Brockington J, Roberson E, Grossman H, Mitsis E, DeToledo-Morrell L, Shah RC, Duara R, Varon D, Greig MT, Roberts P, Albert M, Onyike C, D’Agostino D II, Kielb S, Galvin JE, Cerbone B, Michel CA, Rusinek H, de Leon MJ, Glodzik L, De Santi S, Murali Doraiswamy P, Petrella JR, Wong TZ, Arnold SE, Karlawish JH, Wolk D, Smith CD, Jicha G, Hardy P, Sinha P, Oates E, Conrad G, Lopez OL, Oakley M, Simpson DM, Porsteinsson AP, Goldstein BS, Martin K, Makino KM, Saleem Ismail M, Brand C, Mulnard RA, Thai G, Mc-Adams-Ortiz C, Womack K, Mathews D, Quiceno M, Diaz-Arrastia R, King R, Weiner M, Martin-Cook K, DeVous M, Levey AI, Lah JJ, Cellar JS, Burns JM, Anderson HS, Swerdlow RH, Apostolova L, Tingus K, Woo E, Silverman DHS, Lu PH, Bartzokis G, Graff-Radford NR, Parfitt F, Kendall T, Johnson H, Farlow MR, Hake AM, Matthews BR, Herring S, Hunt C, van Dyck CH, Carson RE, MacAvoy MG, Chertkow H, Bergman H, Hosein C, Black S, Stefanovic B, Caldwell C, Robin Hsiung G-Y, Feldman H, Mudge B, Assaly M, Kertesz A, Rogers J, Bernick C, Munic D, Kerwin D, Mesulam M-M, Lipowski K, Wu C-K, Johnson N, Sadowsky C, Martinez W, Villena T, Scott Turner R, Johnson K, Reynolds B, Sperling RA, Johnson KA, Marshall G, Frey M, Lane B, Rosen A, Tinklenberg J, Sabbagh MN, Belden CM, Jacobson SA, Sirrel SA, Kowall N, Killiany R, Budson AE, Norbash A, Johnson PL, Allard J, Lerner A, Ogrocki P, Hudson L, Fletcher E, Carmichael O, Olichney J, DeCarli C, Kittur S, Borrie M, Lee T-Y, Bartha R, Johnson S, Asthana S, Carlsson CM, Potkin SG, Preda A, Nguyen D, Tariot P, Reeder S, Bates V, Capote H, Rainka M, Scharre DW, Kataki M, Adeli A, Zimmerman EA, Celmins D, Brown AD, Pearlson GD, Blank K, Anderson K, Santulli RB, Kitzmiller TJ, Schwartz ES, Sink KM, Williamson JD, Garg P, Watkins F, Ott BR, Querfurth H, Tremont G, Salloway S, Malloy P, Correia S, Rosen HJ, Miller BL, Mintzer J, Spicer K, Bachman D, Finger E, Pasternak S, Rachinsky I, Drost D, Pomara N, Hernando R, Sarrael A, Schultz SK, Boles Ponto LL, Shim H, Elizabeth Smith K, Relkin N, Chaing G, Raudin L, Smith A, Fargher K, Ashok Raj B, Neylan T, Grafman J, Davis M, Morrison R, Hayes J, Finley S, Friedl K, Fleischman D, Arfanakis K, James O, Massoglia D, Jay Fruehling J, Harding S, Peskind ER, Petrie EC, Li G, Yesavage JA, Taylor JL, Furst AJ. Ferritin levels in the cerebrospinal fluid predict Alzheimer’s disease outcomes and are regulated by APOE. Nat Commun. 2015;6(May):6760.CrossRefPubMedPubMedCentral Ayton S, Faux NG, Bush AI, Weiner MW, Aisen P, Petersen R, Jack CR Jr, Jagust W, Trojanowki JQ, Toga AW, Beckett L, Green RC, Saykin AJ, Morris J, Shaw LM, Khachaturian Z, Sorensen G, Kuller L, Raichle M, Paul S, Davies P, Fillit H, Hefti F, Holtzman D, Marcel Mesulam M, Potter W, Snyder P, Schwartz A, Montine T, Thomas RG, Donohue M, Walter S, Gessert D, Sather T, Jiminez G, Harvey D, Bernstein M, Fox N, Thompson P, Schuff N, Borowski B, Gunter J, Senjem M, Vemuri P, Jones D, Kantarci K, Ward C, Koeppe RA, Foster N, Reiman EM, Chen K, Mathis C, Landau S, Cairns NJ, Householder E, Taylor-Reinwald L, Lee V, Korecka M, Figurski M, Crawford K, Neu S, Foroud TM, Potkin S, Shen L, Faber K, Kim S, Nho K, Thal L, Buckholtz N, Albert M, Frank R, Hsiao J, Kaye J, Quinn J, Lind B, Carter R, Dolen S, Schneider LS, Pawluczyk S, Beccera M, Teodoro L, Spann BM, Brewer J, Vanderswag H, Fleisher A, Heidebrink JL, Lord JL, Mason SS, Albers CS, Knopman D, Johnson K, Doody RS, Villanueva-Meyer J, Chowdhury M, Rountree S, Dang M, Stern Y, Honig LS, Bell KL, Ances B, Carroll M, Leon S, Mintun MA, Schneider S, Oliver A, Marson D, Griffith R, Clark D, Geldmacher D, Brockington J, Roberson E, Grossman H, Mitsis E, DeToledo-Morrell L, Shah RC, Duara R, Varon D, Greig MT, Roberts P, Albert M, Onyike C, D’Agostino D II, Kielb S, Galvin JE, Cerbone B, Michel CA, Rusinek H, de Leon MJ, Glodzik L, De Santi S, Murali Doraiswamy P, Petrella JR, Wong TZ, Arnold SE, Karlawish JH, Wolk D, Smith CD, Jicha G, Hardy P, Sinha P, Oates E, Conrad G, Lopez OL, Oakley M, Simpson DM, Porsteinsson AP, Goldstein BS, Martin K, Makino KM, Saleem Ismail M, Brand C, Mulnard RA, Thai G, Mc-Adams-Ortiz C, Womack K, Mathews D, Quiceno M, Diaz-Arrastia R, King R, Weiner M, Martin-Cook K, DeVous M, Levey AI, Lah JJ, Cellar JS, Burns JM, Anderson HS, Swerdlow RH, Apostolova L, Tingus K, Woo E, Silverman DHS, Lu PH, Bartzokis G, Graff-Radford NR, Parfitt F, Kendall T, Johnson H, Farlow MR, Hake AM, Matthews BR, Herring S, Hunt C, van Dyck CH, Carson RE, MacAvoy MG, Chertkow H, Bergman H, Hosein C, Black S, Stefanovic B, Caldwell C, Robin Hsiung G-Y, Feldman H, Mudge B, Assaly M, Kertesz A, Rogers J, Bernick C, Munic D, Kerwin D, Mesulam M-M, Lipowski K, Wu C-K, Johnson N, Sadowsky C, Martinez W, Villena T, Scott Turner R, Johnson K, Reynolds B, Sperling RA, Johnson KA, Marshall G, Frey M, Lane B, Rosen A, Tinklenberg J, Sabbagh MN, Belden CM, Jacobson SA, Sirrel SA, Kowall N, Killiany R, Budson AE, Norbash A, Johnson PL, Allard J, Lerner A, Ogrocki P, Hudson L, Fletcher E, Carmichael O, Olichney J, DeCarli C, Kittur S, Borrie M, Lee T-Y, Bartha R, Johnson S, Asthana S, Carlsson CM, Potkin SG, Preda A, Nguyen D, Tariot P, Reeder S, Bates V, Capote H, Rainka M, Scharre DW, Kataki M, Adeli A, Zimmerman EA, Celmins D, Brown AD, Pearlson GD, Blank K, Anderson K, Santulli RB, Kitzmiller TJ, Schwartz ES, Sink KM, Williamson JD, Garg P, Watkins F, Ott BR, Querfurth H, Tremont G, Salloway S, Malloy P, Correia S, Rosen HJ, Miller BL, Mintzer J, Spicer K, Bachman D, Finger E, Pasternak S, Rachinsky I, Drost D, Pomara N, Hernando R, Sarrael A, Schultz SK, Boles Ponto LL, Shim H, Elizabeth Smith K, Relkin N, Chaing G, Raudin L, Smith A, Fargher K, Ashok Raj B, Neylan T, Grafman J, Davis M, Morrison R, Hayes J, Finley S, Friedl K, Fleischman D, Arfanakis K, James O, Massoglia D, Jay Fruehling J, Harding S, Peskind ER, Petrie EC, Li G, Yesavage JA, Taylor JL, Furst AJ. Ferritin levels in the cerebrospinal fluid predict Alzheimer’s disease outcomes and are regulated by APOE. Nat Commun. 2015;6(May):6760.CrossRefPubMedPubMedCentral

go back to reference Nanni L, Salvatore C, Cerasa A, Castiglioni I. Combining multiple approaches for the early diagnosis of Alzheimer’s disease. Pattern Recogn Lett. 2016;84:259–66.CrossRef Nanni L, Salvatore C, Cerasa A, Castiglioni I. Combining multiple approaches for the early diagnosis of Alzheimer’s disease. Pattern Recogn Lett. 2016;84:259–66.CrossRef

go back to reference Salvatore C, Cerasa A, Battista P, Gilardi MC, Quattrone A, Castiglioni I. Magnetic resonance imaging biomarkers for the early diagnosis of Alzheimer’s disease: a machine learning approach. Front Neurosci. 2015;9(SEP):1–13. Salvatore C, Cerasa A, Battista P, Gilardi MC, Quattrone A, Castiglioni I. Magnetic resonance imaging biomarkers for the early diagnosis of Alzheimer’s disease: a machine learning approach. Front Neurosci. 2015;9(SEP):1–13.

go back to reference Huang M, Yang W, Feng Q, Chen W, Weiner MW, Aisen P, Petersen R, Jack CR, Jagust W, Trojanowki JQ, Toga AW, Beckett L, Green RC, Saykin AJ, Morris J, Shaw LM, Kaye J, Quinn J, Silbert L, Lind B, Carter R, Dolen S, Schneider LS, Pawluczyk S, Beccera M, Teodoro L, Spann BM, Brewer J, Vanderswag H, Fleisher A, Heidebrink JL, Lord JL, Mason SS, Albers CS, Knopman D, Johnson K, Doody RS, Villanueva-Meyer J, Chowdhury M, Rountree S, Dang M, Stern Y, Honig LS, Bell KL, Ances B, Morris JC, Carroll M, Creech ML, Franklin E, Mintun MA, Schneider S, Oliver A, Marson D, Griffith R, Clark D, Geldmacher D, Brockington J, Roberson E, Love MN, Grossman H, Mitsis E, Shah RC, de Toledo-Morrell L, Duara R, Varon D, Greig MT, Roberts P, Albert M, Onyike C, D’Agostino D, Kielb S, Galvin JE, Cerbone B, Michel CA, Pogorelec DM, Rusinek H, de Leon MJ, Glodzik L, De Santi S, Doraiswamy PM, Petrella JR, Borges-Neto S, Wong TZ, Coleman E, Smith CD, Jicha G, Hardy P, Sinha P, Oates E, Conrad G, Porsteinsson AP, Goldstein BS, Martin K, Makino KM, Ismail MS, Brand C, Mulnard RA, Thai G, Mc-Adams-Ortiz C, Womack K, Mathews D, Quiceno M, Levey AI, Lah JJ, Cellar JS, Burns JM, Swerdlow RH, Brooks WM, Apostolova L, Tingus K, Woo E, Silverman DHS, Lu PH, Bartzokis G, Graff-Radford NR, Parfitt F, Kendall T, Johnson H, Farlow MR, Hake AM, Matthews BR, Brosch JR, Herring S, Hunt C, van Dyck CH, Carson RE, MacAvoy MG, Varma P, Chertkow H, Bergman H, Hosein C, Black S, Stefanovic B, Caldwell C, Hsiung G-YR, Feldman H, Mudge B, Assaly M, Finger E, Pasternack S, Rachisky I, Trost D, Kertesz A, Bernick C, Munic D, Mesulam MM, Lipowski K, Weintraub S, Bonakdarpour B, Kerwin D, Wu C-K, Johnson N, Sadowsky C, Villena T, Turner RS, Johnson K, Reynolds B, Sperling RA, Johnson KA, Marshall G, Yesavage J, Taylor JL, Lane B, Rosen A, Tinklenberg J, Sabbagh MN, Belden CM, Jacobson SA, Sirrel SA, Kowall N, Killiany R, Budson AE, Norbash A, Johnson PL, Obisesan TO, Wolday S, Allard J, Lerner A, Ogrocki P, Tatsuoka C, Fatica P, Fletcher E, Maillard P, Olichney J, DeCarli C, Carmichael O, Kittur S, Borrie M, Lee T-Y, Bartha R, Johnson S, Asthana S, Carlsson CM, Potkin SG, Preda A, Nguyen D, Tariot P, Burke A, Trncic N, Fleisher A, Reeder S, Bates V, Capote H, Rainka M, Scharre DW, Kataki M, Adeli A, Zimmerman EA, Celmins D, Brown AD, Pearlson GD, Blank K, Anderson K, Flashman LA, Seltzer M, Hynes ML, Santulli RB, Sink KM, Gordineer L, Williamson JD, Garg P, Watkins F, Ott BR, Querfurth H, Tremont G, Salloway S, Malloy P, Correia S, Rosen HJ, Miller BL, Perry D, Mintzer J, Spicer K, Bachman D, Pomara N, Hernando R, Sarrael A, Relkin N, Chaing G, Lin M, Ravdin L, Smith A, Raj BA, Fargher K. Longitudinal measurement and hierarchical classification framework for the prediction of Alzheimer’s disease. Sci Rep. 2017;7(August 2016):39880.CrossRefPubMedPubMedCentral Huang M, Yang W, Feng Q, Chen W, Weiner MW, Aisen P, Petersen R, Jack CR, Jagust W, Trojanowki JQ, Toga AW, Beckett L, Green RC, Saykin AJ, Morris J, Shaw LM, Kaye J, Quinn J, Silbert L, Lind B, Carter R, Dolen S, Schneider LS, Pawluczyk S, Beccera M, Teodoro L, Spann BM, Brewer J, Vanderswag H, Fleisher A, Heidebrink JL, Lord JL, Mason SS, Albers CS, Knopman D, Johnson K, Doody RS, Villanueva-Meyer J, Chowdhury M, Rountree S, Dang M, Stern Y, Honig LS, Bell KL, Ances B, Morris JC, Carroll M, Creech ML, Franklin E, Mintun MA, Schneider S, Oliver A, Marson D, Griffith R, Clark D, Geldmacher D, Brockington J, Roberson E, Love MN, Grossman H, Mitsis E, Shah RC, de Toledo-Morrell L, Duara R, Varon D, Greig MT, Roberts P, Albert M, Onyike C, D’Agostino D, Kielb S, Galvin JE, Cerbone B, Michel CA, Pogorelec DM, Rusinek H, de Leon MJ, Glodzik L, De Santi S, Doraiswamy PM, Petrella JR, Borges-Neto S, Wong TZ, Coleman E, Smith CD, Jicha G, Hardy P, Sinha P, Oates E, Conrad G, Porsteinsson AP, Goldstein BS, Martin K, Makino KM, Ismail MS, Brand C, Mulnard RA, Thai G, Mc-Adams-Ortiz C, Womack K, Mathews D, Quiceno M, Levey AI, Lah JJ, Cellar JS, Burns JM, Swerdlow RH, Brooks WM, Apostolova L, Tingus K, Woo E, Silverman DHS, Lu PH, Bartzokis G, Graff-Radford NR, Parfitt F, Kendall T, Johnson H, Farlow MR, Hake AM, Matthews BR, Brosch JR, Herring S, Hunt C, van Dyck CH, Carson RE, MacAvoy MG, Varma P, Chertkow H, Bergman H, Hosein C, Black S, Stefanovic B, Caldwell C, Hsiung G-YR, Feldman H, Mudge B, Assaly M, Finger E, Pasternack S, Rachisky I, Trost D, Kertesz A, Bernick C, Munic D, Mesulam MM, Lipowski K, Weintraub S, Bonakdarpour B, Kerwin D, Wu C-K, Johnson N, Sadowsky C, Villena T, Turner RS, Johnson K, Reynolds B, Sperling RA, Johnson KA, Marshall G, Yesavage J, Taylor JL, Lane B, Rosen A, Tinklenberg J, Sabbagh MN, Belden CM, Jacobson SA, Sirrel SA, Kowall N, Killiany R, Budson AE, Norbash A, Johnson PL, Obisesan TO, Wolday S, Allard J, Lerner A, Ogrocki P, Tatsuoka C, Fatica P, Fletcher E, Maillard P, Olichney J, DeCarli C, Carmichael O, Kittur S, Borrie M, Lee T-Y, Bartha R, Johnson S, Asthana S, Carlsson CM, Potkin SG, Preda A, Nguyen D, Tariot P, Burke A, Trncic N, Fleisher A, Reeder S, Bates V, Capote H, Rainka M, Scharre DW, Kataki M, Adeli A, Zimmerman EA, Celmins D, Brown AD, Pearlson GD, Blank K, Anderson K, Flashman LA, Seltzer M, Hynes ML, Santulli RB, Sink KM, Gordineer L, Williamson JD, Garg P, Watkins F, Ott BR, Querfurth H, Tremont G, Salloway S, Malloy P, Correia S, Rosen HJ, Miller BL, Perry D, Mintzer J, Spicer K, Bachman D, Pomara N, Hernando R, Sarrael A, Relkin N, Chaing G, Lin M, Ravdin L, Smith A, Raj BA, Fargher K. Longitudinal measurement and hierarchical classification framework for the prediction of Alzheimer’s disease. Sci Rep. 2017;7(August 2016):39880.CrossRefPubMedPubMedCentral

go back to reference Maroco J, Silva D, Guerreiro M, De Mendonça A, Santana I. Prediction of dementia patients: a comparative approach using parametric vs.non parametric classifiers. In: Actas do XVII Congresso Anual da Sociedade Portuguesa de Estatística; 2012. p. 241–51. Maroco J, Silva D, Guerreiro M, De Mendonça A, Santana I. Prediction of dementia patients: a comparative approach using parametric vs.non parametric classifiers. In: Actas do XVII Congresso Anual da Sociedade Portuguesa de Estatística; 2012. p. 241–51.

go back to reference Chapman RM, Mapstone M, McCrary JW, Gardner MN, Porsteinsson A, Sandoval TC, Guillily MD, Degrush E, Reilly LA. Predicting conversion from mild cognitive impairment to Alzheimer’s disease using neuropsychological tests and multivariate methods. J Clin Exp Neuropsychol. 2011;33(2):187–99.CrossRefPubMed Chapman RM, Mapstone M, McCrary JW, Gardner MN, Porsteinsson A, Sandoval TC, Guillily MD, Degrush E, Reilly LA. Predicting conversion from mild cognitive impairment to Alzheimer’s disease using neuropsychological tests and multivariate methods. J Clin Exp Neuropsychol. 2011;33(2):187–99.CrossRefPubMed

go back to reference Maroco J, Silva D, Rodrigues A, Guerreiro M, Santana I, de Mendonça A. Data mining methods in the prediction of dementia: a real-data comparison of the accuracy, sensitivity and specificity of linear discriminant analysis, logistic regression, neural networks, support vector machines, classification trees and random forests. BMC Res Notes. 2011;4(1):299.CrossRefPubMedPubMedCentral Maroco J, Silva D, Rodrigues A, Guerreiro M, Santana I, de Mendonça A. Data mining methods in the prediction of dementia: a real-data comparison of the accuracy, sensitivity and specificity of linear discriminant analysis, logistic regression, neural networks, support vector machines, classification trees and random forests. BMC Res Notes. 2011;4(1):299.CrossRefPubMedPubMedCentral

go back to reference Silva D, Guerreiro M, Santana I, Rodrigues A, Cardoso S, Maroco J, de Mendonça A. Prediction of long-term (5 years) conversion to dementia using neuropsychological tests in a memory clinic setting. J Alzheimers Dis. 2013;34(3):681–9.PubMed Silva D, Guerreiro M, Santana I, Rodrigues A, Cardoso S, Maroco J, de Mendonça A. Prediction of long-term (5 years) conversion to dementia using neuropsychological tests in a memory clinic setting. J Alzheimers Dis. 2013;34(3):681–9.PubMed

go back to reference Battista P, Salvatore C, Castiglioni I. Optimizing neuropsychological assessments for cognitive, behavioral, and functional impairment classification: a machine learning study. Behav Neurol. 2017;2017:1850909.CrossRefPubMedPubMedCentral Battista P, Salvatore C, Castiglioni I. Optimizing neuropsychological assessments for cognitive, behavioral, and functional impairment classification: a machine learning study. Behav Neurol. 2017;2017:1850909.CrossRefPubMedPubMedCentral

go back to reference Ewers M, Walsh C, Trojanowski JQ, Shaw LM, Petersen RC, Jack CR, Feldman HH, Bokde ALW, Alexander GE, Scheltens P, Vellas B, Dubois B, Weiner M, Hampel H. Prediction of conversion from mild cognitive impairment to Alzheimer’s disease dementia based upon biomarkers and neuropsychological test performance. Neurobiol Aging. 2012;33(7):1203–14.CrossRefPubMed Ewers M, Walsh C, Trojanowski JQ, Shaw LM, Petersen RC, Jack CR, Feldman HH, Bokde ALW, Alexander GE, Scheltens P, Vellas B, Dubois B, Weiner M, Hampel H. Prediction of conversion from mild cognitive impairment to Alzheimer’s disease dementia based upon biomarkers and neuropsychological test performance. Neurobiol Aging. 2012;33(7):1203–14.CrossRefPubMed

go back to reference Hinrichs C, Singh V, Xu G, Johnson SC. Predictive markers for AD in a multi-modality framework: an analysis of MCI progression in the ADNI population. NeuroImage. 2011;55(2):574–89.CrossRefPubMed Hinrichs C, Singh V, Xu G, Johnson SC. Predictive markers for AD in a multi-modality framework: an analysis of MCI progression in the ADNI population. NeuroImage. 2011;55(2):574–89.CrossRefPubMed

go back to reference Zhou B, Nakatani E, Teramukai S, Nagai Y, Fukushima M. Risk classification in mild cognitive impairment patients for developing alzheimer’s disease. J Alzheimers Dis. 2012;30:367–75.PubMed Zhou B, Nakatani E, Teramukai S, Nagai Y, Fukushima M. Risk classification in mild cognitive impairment patients for developing alzheimer’s disease. J Alzheimers Dis. 2012;30:367–75.PubMed

go back to reference Cui Y, Liu B, Luo S, Zhen X, Fan M, Liu T, Zhu W, Park M, Jiang T, Jin JS. Identification of conversion from mild cognitive impairment to Alzheimer’s disease using multivariate predictors. PLoS One. 2011;6(7):e21896.CrossRefPubMedPubMedCentral Cui Y, Liu B, Luo S, Zhen X, Fan M, Liu T, Zhu W, Park M, Jiang T, Jin JS. Identification of conversion from mild cognitive impairment to Alzheimer’s disease using multivariate predictors. PLoS One. 2011;6(7):e21896.CrossRefPubMedPubMedCentral

go back to reference Espinosa A, Alegret M, Valero S, Vinyes-Junqué G, Hernández I, Mauleón A, Rosende-Roca M, Ruiz A, López O, Tárraga L, Boada M. A longitudinal follow-up of 550 mild cognitive impairment patients: evidence for large conversion to dementia rates and detection of major risk factors involved. J Alzheimers Dis. 2013;34(3):769–80.PubMed Espinosa A, Alegret M, Valero S, Vinyes-Junqué G, Hernández I, Mauleón A, Rosende-Roca M, Ruiz A, López O, Tárraga L, Boada M. A longitudinal follow-up of 550 mild cognitive impairment patients: evidence for large conversion to dementia rates and detection of major risk factors involved. J Alzheimers Dis. 2013;34(3):769–80.PubMed

go back to reference Samtani NM, Raghavan N, Novak G, Nandini R, Narayan VA. Disease progression model for clinical dementia rating – sum of boxes in mild cognitive impairment and Alzheimer ’ s subjects from the Alzheimer ’ s disease Neuroimaging initiative. Neuropsychiatr Dis Treat. 2014;10:929–52.CrossRefPubMedPubMedCentral Samtani NM, Raghavan N, Novak G, Nandini R, Narayan VA. Disease progression model for clinical dementia rating – sum of boxes in mild cognitive impairment and Alzheimer ’ s subjects from the Alzheimer ’ s disease Neuroimaging initiative. Neuropsychiatr Dis Treat. 2014;10:929–52.CrossRefPubMedPubMedCentral

go back to reference Carreiro AV, Mendonça A, de Carvalho M, Madeira SC. Integrative biomarker discovery in neurodegenerative diseases. Wiley Interdiscip Rev Syst Biol Med. 2015;7(6):357–79.CrossRefPubMed Carreiro AV, Mendonça A, de Carvalho M, Madeira SC. Integrative biomarker discovery in neurodegenerative diseases. Wiley Interdiscip Rev Syst Biol Med. 2015;7(6):357–79.CrossRefPubMed

go back to reference Cabral C, Morgado PM, Campos Costa D, Silveira M. Predicting conversion from MCI to AD with FDG-PET brain images at different prodromal stages. Comput Biol Med. 2015;58:101–9.CrossRefPubMed Cabral C, Morgado PM, Campos Costa D, Silveira M. Predicting conversion from MCI to AD with FDG-PET brain images at different prodromal stages. Comput Biol Med. 2015;58:101–9.CrossRefPubMed

go back to reference Eskildsen SF, Coupé P, García-Lorenzo D, Fonov V, Pruessner JC, Collins DL. Prediction of Alzheimer’s disease in subjects with mild cognitive impairment from the ADNI cohort using patterns of cortical thinning. NeuroImage. 2013;65:511–21.CrossRefPubMed Eskildsen SF, Coupé P, García-Lorenzo D, Fonov V, Pruessner JC, Collins DL. Prediction of Alzheimer’s disease in subjects with mild cognitive impairment from the ADNI cohort using patterns of cortical thinning. NeuroImage. 2013;65:511–21.CrossRefPubMed

go back to reference Adaszewski S, Dukart J, Kherif F, Frackowiak R, Draganski B. How early can we predict Alzheimer’s disease using computational anatomy? Neurobiol Aging. 2013;34(12):2815–26.CrossRefPubMed Adaszewski S, Dukart J, Kherif F, Frackowiak R, Draganski B. How early can we predict Alzheimer’s disease using computational anatomy? Neurobiol Aging. 2013;34(12):2815–26.CrossRefPubMed

go back to reference Jack CR, Knopman DS, Jagust WJ, Shaw LM, Aisen PS, Weiner MW, Petersen RC, Trojanowski JQ. Hypothetical model of dynamic biomarkers of the Alzheimer’s pathological cascade. Lancet Neurol. 2010;9(1):119–28.CrossRefPubMedPubMedCentral Jack CR, Knopman DS, Jagust WJ, Shaw LM, Aisen PS, Weiner MW, Petersen RC, Trojanowski JQ. Hypothetical model of dynamic biomarkers of the Alzheimer’s pathological cascade. Lancet Neurol. 2010;9(1):119–28.CrossRefPubMedPubMedCentral

go back to reference Guerreiro M. Contributo da Neuropsicologia para o Estudo das Demências. Portugal: Faculty of Medicine of Lisbon; 1998. Guerreiro M. Contributo da Neuropsicologia para o Estudo das Demências. Portugal: Faculty of Medicine of Lisbon; 1998.

go back to reference American Psychiatric Association. DSM-IV-TR. 4th ed. Washington DC: APA; 2000. American Psychiatric Association. DSM-IV-TR. 4th ed. Washington DC: APA; 2000.

go back to reference Portet F, Ousset P, Visser P, Frisoni G, Nobili F, Scheltens P, Vellas B, Touchon J, M. W. G. of the E. C. on A. D. (EADC). Mild cognitive impairment (MCI) in medical practice: a critical review of the concept and new diagnostic procedure. Report of the MCI working Group of the European Consortium on Alzheimer’s disease. J Neurol Neurosurg Psychiatry. 2006;77(6):714–8.CrossRefPubMedPubMedCentral Portet F, Ousset P, Visser P, Frisoni G, Nobili F, Scheltens P, Vellas B, Touchon J, M. W. G. of the E. C. on A. D. (EADC). Mild cognitive impairment (MCI) in medical practice: a critical review of the concept and new diagnostic procedure. Report of the MCI working Group of the European Consortium on Alzheimer’s disease. J Neurol Neurosurg Psychiatry. 2006;77(6):714–8.CrossRefPubMedPubMedCentral

go back to reference Frank E, Hall M, Trigg L, Holmes G, Witten IH. Data mining in bioinformatics using Weka. Bioinformatics. 2004;20(15):2479–81.CrossRefPubMed Frank E, Hall M, Trigg L, Holmes G, Witten IH. Data mining in bioinformatics using Weka. Bioinformatics. 2004;20(15):2479–81.CrossRefPubMed

go back to reference Powers DMW. Evaluation: from precision, recall and F-factor to ROC, Informedness, Markedness & Correlation. Adelaide; 2007. Powers DMW. Evaluation: from precision, recall and F-factor to ROC, Informedness, Markedness & Correlation. Adelaide; 2007.

go back to reference Hall MA. Correlation-based feature selection for machine learning. Hamilton: University of Waikato; 1999. Hall MA. Correlation-based feature selection for machine learning. Hamilton: University of Waikato; 1999.

go back to reference Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP. SMOTE : synthetic minority over-sampling technique. J Artif Intell Res. 2002;16:321–57. Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP. SMOTE : synthetic minority over-sampling technique. J Artif Intell Res. 2002;16:321–57.

go back to reference Demsar J. Statistical comparisons of classifiers over multiple data sets. J Mach Learn Res. 2006;7:1–30. Demsar J. Statistical comparisons of classifiers over multiple data sets. J Mach Learn Res. 2006;7:1–30.

go back to reference Nunes C, Silva D, Guerreiro M, de Mendonça A, Carvalho AM, Madeira SC. Class imbalance in the prediction of dementia from neuropsychological data. Portuguese Conference on Artificial Intelligence. 2013;1:138–51. Nunes C, Silva D, Guerreiro M, de Mendonça A, Carvalho AM, Madeira SC. Class imbalance in the prediction of dementia from neuropsychological data. Portuguese Conference on Artificial Intelligence. 2013;1:138–51.

go back to reference de Mendonça A. Rethinking Alzheimer’s disease. Front Neurol. 2012;3:45.CrossRefPubMedPubMedCentral de Mendonça A. Rethinking Alzheimer’s disease. Front Neurol. 2012;3:45.CrossRefPubMedPubMedCentral