Mining relationships between multi-modal data to characterize Alzheimer’s disease manifestation

ilustraciones, diagramas

Autores:: Pabón Ochoa, German Alejandro

Tipo de recurso:

Fecha de publicación:: 2024

Institución:: Universidad Nacional de Colombia

Repositorio:: Universidad Nacional de Colombia

Idioma:: eng

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dc.title.eng.fl_str_mv	Mining relationships between multi-modal data to characterize Alzheimer’s disease manifestation
dc.title.translated.spa.fl_str_mv	Extracción de relaciones entre datos multimodales para caracterizar la manifestación de la enfermedad de Alzheimer
title	Mining relationships between multi-modal data to characterize Alzheimer’s disease manifestation
spellingShingle	Mining relationships between multi-modal data to characterize Alzheimer’s disease manifestation 610 - Medicina y salud::616 - Enfermedades 600 - Tecnología (Ciencias aplicadas)::607 - Educación, investigación, temas relacionados 600 - Tecnología (Ciencias aplicadas)::607 - Educación, investigación, temas relacionados Disfunción Cognitiva Cognitive Dysfunction Alzheimer’s Disease Mild Cognitive Impairment Neuropsychological Test Prediction Disease Progression Quantitative Characterization Voxel-Based Morphometry Computational Neuroscience Enfermedad de Alzheimer Deterioro Cognitivo Leve Pruebas Neuropsicológicas Predicción Progresión de la Enfermedad Caracterización Cuantitativa Morfometría Basada en Vóxeles Neurociencia Computacional
title_short	Mining relationships between multi-modal data to characterize Alzheimer’s disease manifestation
title_full	Mining relationships between multi-modal data to characterize Alzheimer’s disease manifestation
title_fullStr	Mining relationships between multi-modal data to characterize Alzheimer’s disease manifestation
title_full_unstemmed	Mining relationships between multi-modal data to characterize Alzheimer’s disease manifestation
title_sort	Mining relationships between multi-modal data to characterize Alzheimer’s disease manifestation
dc.creator.fl_str_mv	Pabón Ochoa, German Alejandro
dc.contributor.advisor.none.fl_str_mv	Romero, Eduardo Giraldo Franco, Diana Lorena
dc.contributor.author.none.fl_str_mv	Pabón Ochoa, German Alejandro
dc.contributor.researchgroup.spa.fl_str_mv	Cim@Lab
dc.subject.ddc.spa.fl_str_mv	610 - Medicina y salud::616 - Enfermedades 600 - Tecnología (Ciencias aplicadas)::607 - Educación, investigación, temas relacionados 600 - Tecnología (Ciencias aplicadas)::607 - Educación, investigación, temas relacionados
topic	610 - Medicina y salud::616 - Enfermedades 600 - Tecnología (Ciencias aplicadas)::607 - Educación, investigación, temas relacionados 600 - Tecnología (Ciencias aplicadas)::607 - Educación, investigación, temas relacionados Disfunción Cognitiva Cognitive Dysfunction Alzheimer’s Disease Mild Cognitive Impairment Neuropsychological Test Prediction Disease Progression Quantitative Characterization Voxel-Based Morphometry Computational Neuroscience Enfermedad de Alzheimer Deterioro Cognitivo Leve Pruebas Neuropsicológicas Predicción Progresión de la Enfermedad Caracterización Cuantitativa Morfometría Basada en Vóxeles Neurociencia Computacional
dc.subject.decs.none.fl_str_mv	Disfunción Cognitiva Cognitive Dysfunction
dc.subject.proposal.eng.fl_str_mv	Alzheimer’s Disease Mild Cognitive Impairment Neuropsychological Test Prediction Disease Progression Quantitative Characterization Voxel-Based Morphometry Computational Neuroscience
dc.subject.proposal.spa.fl_str_mv	Enfermedad de Alzheimer Deterioro Cognitivo Leve Pruebas Neuropsicológicas Predicción Progresión de la Enfermedad Caracterización Cuantitativa Morfometría Basada en Vóxeles Neurociencia Computacional
description	ilustraciones, diagramas
publishDate	2024
dc.date.accessioned.none.fl_str_mv	2024-07-02T20:51:41Z
dc.date.available.none.fl_str_mv	2024-07-02T20:51:41Z
dc.date.issued.none.fl_str_mv	2024-06-28
dc.type.spa.fl_str_mv	Trabajo de grado - Maestría
dc.type.driver.spa.fl_str_mv	info:eu-repo/semantics/masterThesis
dc.type.version.spa.fl_str_mv	info:eu-repo/semantics/acceptedVersion
dc.type.content.spa.fl_str_mv	Text
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dc.identifier.uri.none.fl_str_mv	https://repositorio.unal.edu.co/handle/unal/86352
dc.identifier.instname.spa.fl_str_mv	Universidad Nacional de Colombia
dc.identifier.repo.none.fl_str_mv	Repositorio Institucional Universidad Nacional de Colombia
dc.identifier.repourl.spa.fl_str_mv	https://repositorio.unal.edu.co/
url	https://repositorio.unal.edu.co/handle/unal/86352 https://repositorio.unal.edu.co/
identifier_str_mv	Universidad Nacional de Colombia Repositorio Institucional Universidad Nacional de Colombia
dc.language.iso.spa.fl_str_mv	eng
language	eng
dc.relation.references.spa.fl_str_mv	Alzheimer’s Association et al. 2020 alzheimer’s disease facts and figures. Alzheimer’s & Dementia, 16(3):391+, 2020. Alireza Atri. The alzheimers disease clinical spectrum: diagnosis and management. Medical Clinics, 103(2):263–293, 2019. Colin L. Masters, Randall Bateman, Kaj Blennow, Christopher C. Rowe, Reisa A. Sperling, and Jeffrey L. Cummings. Alzheimer’s disease. Nature Reviews Disease Primers, 1:15056, 2015. Maria Ferretti, M Florencia Iulita, Enrica Cavedo, Patrizia Chiesa, Annemarie Schumacher Dimech, Antonella Chadha, Francesca Baracchi, H´el`ene Girouard, Sabina Misoch, Ezio Giacobini, Herman Depypere, and Harald Hampel. Sex differences in alzheimer disease — the gateway to precision medicine. Nature Reviews Neurology, 14, 07 2018. Martin James Prince, Anders Wimo, Maelenn Mari Guerchet, Gemma Claire Ali, Yu- Tzu Wu, and Matthew Prina. World Alzheimer Report 2015-The Global Impact of Dementia: An analysis of prevalence, incidence, cost and trends. Alzheimer’s Disease International, 2015. Zhang Daoqiang, Shen Dinggang, and The Alzheimer’s Disease Neuroimaging Initiative. Multi-modal multi-task learning for joint prediction of multiple regression and classification variables in alzheimer’s disease. NeuroImage, 59(2):895–907, 2012. Serra Angela, Galdi Paola, and Tagliaferri Roberto. Multi-view learning in biomedical applications. In Artificial Intelligence in the Age of Neural Networks and Brain Computing, pages 265–280. Elsevier, 2019. Oskar Hansson, John Seibyl, Erik Stomrud, Henrik Zetterberg, John Q. Trojanowski, Tobias Bittner, Valeria Lifke, Veronika Corradini, Udo Eichenlaub, Richard Batrla, Katharina Buck, Katharina Zink, Christina Rabe, Kaj Blennow, and Leslie M. Shaw. Csf biomarkers of alzheimer’s disease concord with amyloid-β pet and predict clinical progression: A study of fully automated immunoassays in biofinder and adni cohorts. Alzheimer’s & Dementia, 14(11):1470–1481, 2018. Joey Annette Contreras, Vahan Aslanyan, Melanie D. Sweeney, Lianne M.J. Sanders, Abhay P. Sagare, Berislav V. Zlokovic, Arthur W. Toga, S. Duke Han, John C. Morris, Anne Fagan, Parinaz Massoumzadeh, Tammie L. Benzinger, and Judy Pa. Functional connectivity among brain regions affected in alzheimer’s disease is associated with csf tnf- α in apoe4 carriers. Neurobiology of Aging, 86:112–122, 2020. Christian Humpel. Identifying and validating biomarkers for alzheimer’s disease. Trends in biotechnology, 29(1):26–32, 2011. Douglas W Scharre. Preclinical, prodromal, and dementia stages of alzheimer’s disease. Pract Neurol, pages 36–47, 2019. Berndt Winblad, Katie Palmer, Miia Kivipelto, Vesna Jelic, Laura Fratiglioni, L-O Wahlund, Agneta Nordberg, Lars B¨ackman, Michael Albert, Ove Almkvist, et al. Mild cognitive impairment–beyond controversies, towards a consensus: report of the international working group on mild cognitive impairment. Journal of internal medicine, 256(3):240–246, 2004. Reisa A Sperling, Jason Karlawish, and Keith A Johnson. Preclinical alzheimer disease— the challenges ahead. Nature Reviews Neurology, 9(1):54–58, 2013. Emily C. Edmonds, Carrie R. McDonald, Anisa Marshall, Kelsey R. Thomas, Joel Eppig, Alexandra J. Weigand, Lisa Delano-Wood, Douglas R. Galasko, David P. Salmon, and Mark W. Bondi. Early versus late mci: Improved mci staging using a neuropsychological approach. Alzheimer’s & Dementia, 15(5):699–708, 2019. Ronald C Petersen, Glenn E Smith, Stephen CWaring, Robert J Ivnik, Eric G Tangalos, and Emre Kokmen. Mild cognitive impairment: clinical characterization and outcome. Archives of neurology, 56(3):303–308, 1999. Joel S. Eppig, Emily C. Edmonds, Laura Campbell, Mark Sanderson-Cimino, Lisa Delano-Wood, and Mark W. Bondi. Statistically derived subtypes and associations with cerebrospinal fluid and genetic biomarkers in mild cognitive impairment: A latent profile analysis. Journal of the International Neuropsychological Society, 23(7):564–576, 2017. Nanbo Sun, Elizabeth C. Mormino, Jianzhong Chen, Mert R. Sabuncu, and B.T. Thomas Yeo. Multi-modal latent factor exploration of atrophy, cognitive and tau heterogeneity in alzheimer’s disease. NeuroImage, 201:116043, 2019. Charles DeCarli. Mild cognitive impairment: prevalence, prognosis, aetiology, and treatment. The Lancet Neurology, 2(1):15–21, 2003. Ronald C Petersen. Mild cognitive impairment as a diagnostic entity. Journal of internal medicine, 256(3):183–194, 2004. Devendra Goyal, Donna Tjandra, Raymond Q Migrino, Bruno Giordani, Zeeshan Syed, Jenna Wiens, Alzheimer’s Disease Neuroimaging Initiative, et al. Characterizing heterogeneity in the progression of alzheimer’s disease using longitudinal clinical and neuroimaging biomarkers. Alzheimer’s & Dementia: Diagnosis, Assessment & Disease Monitoring, 10:629–637, 2018. Ronald C Petersen, Rosebud O Roberts, David S Knopman, Bradley F Boeve, Yonas E Geda, Robert J Ivnik, Glenn E Smith, and Clifford R Jack. Mild cognitive impairment: ten years later. Archives of neurology, 66(12):1447–1455, 2009. Emily C Edmonds, Joel Eppig, Mark W Bondi, Kelly M Leyden, Bailey Goodwin, Lisa Delano-Wood, Carrie R McDonald, Alzheimer’s Disease Neuroimaging Initiative, et al. Heterogeneous cortical atrophy patterns in mci not captured by conventional diagnostic criteria. Neurology, 87(20):2108–2116, 2016. Emily C Edmonds, Lisa Delano-Wood, Lindsay R Clark, Amy J Jak, Daniel A Nation, Carrie R McDonald, David J Libon, Rhoda Au, Douglas Galasko, David P Salmon, et al. Susceptibility of the conventional criteria for mild cognitive impairment to false-positive diagnostic errors. Alzheimer’s & Dementia, 11(4):415–424, 2015. Tiffany F Hughes, Beth E Snitz, and Mary Ganguli. Should mild cognitive impairment be subtyped? Current opinion in psychiatry, 24(3):237–242, 2011. Lisa Delano-Wood, Mark W Bondi, Joshua Sacco, Norm Abeles, Amy J Jak, David J Libon, and Andrea Bozoki. Heterogeneity in mild cognitive impairment: Differences in neuropsychological profile and associated white matter lesion pathology. Journal of the International Neuropsychological Society, 15(6):906–914, 2009. David J Libon, Sharon X Xie, Joel Eppig, Graham Wicas, Melissa Lamar, Carol Lippa, Brianne M Bettcher, Catherine C Price, Tania Giovannetti, Rod Swenson, et al. The heterogeneity of mild cognitive impairment: A neuropsychological analysis. Journal of the International Neuropsychological Society, 16(1):84–93, 2010. Mary M Machulda, Emily S Lundt, Sabrina M Albertson,Walter K Kremers, Michelle M Mielke, David S Knopman, Mark W Bondi, and Ronald C Petersen. Neuropsychological subtypes of incident mild cognitive impairment in the mayo clinic study of aging. Alzheimer’s & Dementia, 15(7):878–887, 2019. Diana L Giraldo, Jan Sijbers, Eduardo Romero, and Alzheimer’s Disease Neuroimaging Initiative. Quantification of cognitive impairment to characterize heterogeneity of patients at risk of developing alzheimer’s disease dementia. Alzheimer’s & Dementia: Diagnosis, Assessment & Disease Monitoring, 13(1):e12237, 2021. Mary M Machulda, Emily S Lundt, Sabrina M Albertson, Anthony J Spychalla, Christopher G Schwarz, Michelle M Mielke, Clifford R Jack Jr,Walter K Kremers, Prashanthi Vemuri, David S Knopman, et al. Cortical atrophy patterns of incident mci subtypes in the mayo clinic study of aging. Alzheimer’s & Dementia, 16(7):1013–1022, 2020. John Ashburner and Karl J Friston. Voxel-based morphometry—the methods. Neuroimage, 11(6):805–821, 2000. John Ashburner and Karl J Friston. Why voxel-based morphometry should be used. Neuroimage, 14(6):1238–1243, 2001. R. C. Petersen, P. S. Aisen, L. A. Beckett, M. C. Donohue, A. C. Gamst, D. J. Harvey, C. R. Jack, W. J. Jagust, L. M. Shaw, A. W. Toga, J. Q. Trojanowski, and M. W. Weiner. Alzheimer’s disease neuroimaging initiative (adni) clinical characterization. Neurology, 74(3):201–209, 2010. Maura Mitrushina, Kyle B Boone, Jill Razani, and Louis F D’Elia. Handbook of normative data for neuropsychological assessment. Oxford University Press, 2005. Joe Ward. Hierarchical grouping to optimize an objective function. Journal of the American Statistical Association, 58(301):236–244, 1963. Peter J. Rousseeuw. Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. Journal of Computational and Applied Mathematics, 20:53–65, 1987. J. Ashburner and K.J. Friston. Voxel based morphometry. In Larry R. Squire, editor, Encyclopedia of Neuroscience, pages 471–477. Academic Press, Oxford, 2009. Edmund T Rolls, Chu-Chung Huang, Ching-Po Lin, Jianfeng Feng, and Marc Joliot. Automated anatomical labelling atlas 3. Neuroimage, 206:116189, 2020. Martina Zverova. Alzheimer’s disease and blood-based biomarkers–potential contexts of use. Neuropsychiatric disease and treatment, 14:1877–1882, 2018. German A Pabón, Diana L Giraldo, and Eduardo Romero. Mining relations between neuropsychological data to characterize alzheimer’s disease. In 17th International Symposium on Medical Information Processing and Analysis, volume 12088, pages 190–197. SPIE, 2021. German A. Pabón Ochoa, Diana L. Giraldo, and Eduardo Romero. Exploring anatomical brain heterogeneity in subjects with mild cognitive impairment. In 2023 19th International Symposium on Medical Information Processing and Analysis (SIPAIM), pages 1–5, 2023.
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dc.format.extent.spa.fl_str_mv	vii, 34 páginas
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dc.publisher.spa.fl_str_mv	Universidad Nacional de Colombia
dc.publisher.program.spa.fl_str_mv	Bogotá - Medicina - Maestría en Ingeniería Biomédica
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dc.publisher.place.spa.fl_str_mv	Bogotá, Colombia
dc.publisher.branch.spa.fl_str_mv	Universidad Nacional de Colombia - Sede Bogotá
institution	Universidad Nacional de Colombia
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spelling	Atribución-NoComercial 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Romero, Eduardo5e1ccc896b1dc02412a70e287bf37176Giraldo Franco, Diana Lorena8891910c9ca0afd11bb02a8660d581c0Pabón Ochoa, German Alejandro4da16a45a530351a919180c0f45b56e9Cim@Lab2024-07-02T20:51:41Z2024-07-02T20:51:41Z2024-06-28https://repositorio.unal.edu.co/handle/unal/86352Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/ilustraciones, diagramasLa heterogeneidad en la manifestación clínica del deterioro cognitivo leve (MCI, por sus siglas en inglés) plantea un desafío significativo. Una caracterización integral de la enfermedad de Alzheimer (AD, por sus siglas en inglés) en esta etapa temprana permite la detección oportuna, la predicción de la progresión de la enfermedad y, en consecuencia, la intervención y el monitoreo antes del diagnóstico clínico de demencia. Presentamos una estrategia cuantitativa para caracterizar alteraciones neuropsicológicas en pacientes con MCI en riesgo de desarrollar demencia por AD, utilizando datos de Alzheimer’s Disease Neuroimaging Initiative (ADNI). Un conjunto de variables de pruebas cognitivas, funcionales y conductuales fue seleccionado de una muestra de pacientes con deterioro cognitivo. El análisis del rendimiento anormal y el uso de métricas relacionales nos permitieron identificar cinco grupos de elementos que podrían representar posibles dimensiones neuropsicológicas de la enfermedad y que podrían utilizarse para describir cuantitativamente a un individuo con deterioro cognitivo. Estas características están representadas por diferentes dominios cognitivos y funcionales: 1) Praxis constructiva, 2) Orientación, Memoria y tareas de la vida diaria, 3) Lenguaje, 4) Atención, y 5) Tareas de velocidad de procesamiento y funciones ejecutivas. La proporción de variables exhibidas dentro de cada característica cuantifica la anormalidad neuropsicológica. La utilidad de la caracterización propuesta fue evaluada mediante dos tareas. En primer lugar, prediciendo la progresión de la enfermedad desde la MCI hasta la demencia de Alzheimer. Entrenamos y probamos un Clasificador de Máquina de Vectores de Soporte, logrando una precisión del 0,76 dentro de los 36 meses. En segundo lugar, identificando subgrupos de MCI que exhibieron perfiles neuropsicológicos diversos y diferentes patrones de atrofia de materia gris. Individuos diagnosticados con MCI fueron divididos en siete subgrupos. Al comparar con individuos cognitivamente normales, el análisis utilizando Morfometría Basada en Vóxeles reveló regiones cerebrales específicas con diferencias significativas. Observamos una estrecha co-ocurrencia entre los deterioros cognitivos y los cambios estructurales. A medida que aumentaban las anormalidades cognitivas y conductuales, estas se asociaban con patrones más extensos de atrofia de materia gris. Este trabajo ofrece un enfoque alternativo para caracterizar cuantitativamente los subtipos de MCI y comprender los patrones neurodegenerativos, proporcionando información valiosa para una mejor caracterización en la etapa prodrómica de la enfermedad de Alzheimer. (Texto tomado de la fuente)The heterogeneity in the clinical manifestation of Mild Cognitive Impairment (MCI) poses a significant challenge. A comprehensive characterization of Alzheimer’s Disease (AD) in this early stage allows for timely detection, prediction of disease progression, and, consequently, intervention and monitoring before clinical diagnosis of dementia. We present a quantitative strategy to characterize neuropsychological alterations in MCI patients at risk of developing AD dementia using data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). A set of items from cognitive, functional, and behavioral tests was selected from a sample of cognitively impaired patients. The analysis of abnormal performance and the use of relational metrics allowed us to identify five clusters of items that could represent possible neuropsychological dimensions and could be used to quantitatively describe a cognitively impaired individual. These characteristics are represented by different cognitive and functional domains: 1) constructional praxis; 2) orientation, memory, and daily living tasks; 3) language; 4) attention; and 5) processing speed tasks and executive functions. The proportion of variables exhibited within each characteristic quantifies the neuropsychological abnormality. The utility of the proposed characterization was evaluated by two tasks. Firstly, predicting disease progression from MCI to AD dementia. We trained and tested a Support Vector Machine Classifier, achieving an accuracy of 0.76 within 36 months. Secondly, identifying MCI subgroups that exhibit diverse neuropsychological profiles and different patterns of gray matter atrophy. Individuals diagnosed with MCI were partitioned into seven subgroups. Upon comparison with cognitively normal individuals, the analysis using Voxel-Based Morphometry revealed specific brain regions with significant differences. We observed a close co-occurrence between cognitive impairments and structural changes. As cognitive and behavioral abnormalities increased, they were associated with more extensive patterns of gray matter atrophy. This work offers an alternative approach to quantitatively characterize MCI subtypes and comprehend neurodegenerative patterns, providing valuable insights for enhanced characterization in the prodromal stage of Alzheimer’s Disease.MaestríaMagíster en Ingeniería BiomédicaDigital Anatomy by Imagesvii, 34 páginasapplication/pdfengUniversidad Nacional de ColombiaBogotá - Medicina - Maestría en Ingeniería BiomédicaFacultad de MedicinaBogotá, ColombiaUniversidad Nacional de Colombia - Sede Bogotá610 - Medicina y salud::616 - Enfermedades600 - Tecnología (Ciencias aplicadas)::607 - Educación, investigación, temas relacionados600 - Tecnología (Ciencias aplicadas)::607 - Educación, investigación, temas relacionadosDisfunción CognitivaCognitive DysfunctionAlzheimer’s DiseaseMild Cognitive ImpairmentNeuropsychological TestPredictionDisease ProgressionQuantitative CharacterizationVoxel-Based MorphometryComputational NeuroscienceEnfermedad de AlzheimerDeterioro Cognitivo LevePruebas NeuropsicológicasPredicciónProgresión de la EnfermedadCaracterización CuantitativaMorfometría Basada en VóxelesNeurociencia ComputacionalMining relationships between multi-modal data to characterize Alzheimer’s disease manifestationExtracción de relaciones entre datos multimodales para caracterizar la manifestación de la enfermedad de AlzheimerTrabajo de grado - Maestríainfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/acceptedVersionTexthttp://purl.org/redcol/resource_type/TMAlzheimer’s Association et al. 2020 alzheimer’s disease facts and figures. Alzheimer’s & Dementia, 16(3):391+, 2020.Alireza Atri. The alzheimers disease clinical spectrum: diagnosis and management. Medical Clinics, 103(2):263–293, 2019.Colin L. Masters, Randall Bateman, Kaj Blennow, Christopher C. Rowe, Reisa A. Sperling, and Jeffrey L. Cummings. Alzheimer’s disease. Nature Reviews Disease Primers, 1:15056, 2015.Maria Ferretti, M Florencia Iulita, Enrica Cavedo, Patrizia Chiesa, Annemarie Schumacher Dimech, Antonella Chadha, Francesca Baracchi, H´el`ene Girouard, Sabina Misoch, Ezio Giacobini, Herman Depypere, and Harald Hampel. Sex differences in alzheimer disease — the gateway to precision medicine. Nature Reviews Neurology, 14, 07 2018.Martin James Prince, Anders Wimo, Maelenn Mari Guerchet, Gemma Claire Ali, Yu- Tzu Wu, and Matthew Prina. World Alzheimer Report 2015-The Global Impact of Dementia: An analysis of prevalence, incidence, cost and trends. Alzheimer’s Disease International, 2015.Zhang Daoqiang, Shen Dinggang, and The Alzheimer’s Disease Neuroimaging Initiative. Multi-modal multi-task learning for joint prediction of multiple regression and classification variables in alzheimer’s disease. NeuroImage, 59(2):895–907, 2012.Serra Angela, Galdi Paola, and Tagliaferri Roberto. Multi-view learning in biomedical applications. In Artificial Intelligence in the Age of Neural Networks and Brain Computing, pages 265–280. Elsevier, 2019.Oskar Hansson, John Seibyl, Erik Stomrud, Henrik Zetterberg, John Q. Trojanowski, Tobias Bittner, Valeria Lifke, Veronika Corradini, Udo Eichenlaub, Richard Batrla, Katharina Buck, Katharina Zink, Christina Rabe, Kaj Blennow, and Leslie M. Shaw. Csf biomarkers of alzheimer’s disease concord with amyloid-β pet and predict clinical progression: A study of fully automated immunoassays in biofinder and adni cohorts. Alzheimer’s & Dementia, 14(11):1470–1481, 2018.Joey Annette Contreras, Vahan Aslanyan, Melanie D. Sweeney, Lianne M.J. Sanders, Abhay P. Sagare, Berislav V. Zlokovic, Arthur W. Toga, S. Duke Han, John C. Morris, Anne Fagan, Parinaz Massoumzadeh, Tammie L. Benzinger, and Judy Pa. Functional connectivity among brain regions affected in alzheimer’s disease is associated with csf tnf- α in apoe4 carriers. Neurobiology of Aging, 86:112–122, 2020.Christian Humpel. Identifying and validating biomarkers for alzheimer’s disease. Trends in biotechnology, 29(1):26–32, 2011.Douglas W Scharre. Preclinical, prodromal, and dementia stages of alzheimer’s disease. Pract Neurol, pages 36–47, 2019.Berndt Winblad, Katie Palmer, Miia Kivipelto, Vesna Jelic, Laura Fratiglioni, L-O Wahlund, Agneta Nordberg, Lars B¨ackman, Michael Albert, Ove Almkvist, et al. Mild cognitive impairment–beyond controversies, towards a consensus: report of the international working group on mild cognitive impairment. Journal of internal medicine, 256(3):240–246, 2004.Reisa A Sperling, Jason Karlawish, and Keith A Johnson. Preclinical alzheimer disease— the challenges ahead. Nature Reviews Neurology, 9(1):54–58, 2013.Emily C. Edmonds, Carrie R. McDonald, Anisa Marshall, Kelsey R. Thomas, Joel Eppig, Alexandra J. Weigand, Lisa Delano-Wood, Douglas R. Galasko, David P. Salmon, and Mark W. Bondi. Early versus late mci: Improved mci staging using a neuropsychological approach. Alzheimer’s & Dementia, 15(5):699–708, 2019.Ronald C Petersen, Glenn E Smith, Stephen CWaring, Robert J Ivnik, Eric G Tangalos, and Emre Kokmen. Mild cognitive impairment: clinical characterization and outcome. Archives of neurology, 56(3):303–308, 1999.Joel S. Eppig, Emily C. Edmonds, Laura Campbell, Mark Sanderson-Cimino, Lisa Delano-Wood, and Mark W. Bondi. Statistically derived subtypes and associations with cerebrospinal fluid and genetic biomarkers in mild cognitive impairment: A latent profile analysis. Journal of the International Neuropsychological Society, 23(7):564–576, 2017.Nanbo Sun, Elizabeth C. Mormino, Jianzhong Chen, Mert R. Sabuncu, and B.T. Thomas Yeo. Multi-modal latent factor exploration of atrophy, cognitive and tau heterogeneity in alzheimer’s disease. NeuroImage, 201:116043, 2019.Charles DeCarli. Mild cognitive impairment: prevalence, prognosis, aetiology, and treatment. The Lancet Neurology, 2(1):15–21, 2003.Ronald C Petersen. Mild cognitive impairment as a diagnostic entity. Journal of internal medicine, 256(3):183–194, 2004.Devendra Goyal, Donna Tjandra, Raymond Q Migrino, Bruno Giordani, Zeeshan Syed, Jenna Wiens, Alzheimer’s Disease Neuroimaging Initiative, et al. Characterizing heterogeneity in the progression of alzheimer’s disease using longitudinal clinical and neuroimaging biomarkers. Alzheimer’s & Dementia: Diagnosis, Assessment & Disease Monitoring, 10:629–637, 2018.Ronald C Petersen, Rosebud O Roberts, David S Knopman, Bradley F Boeve, Yonas E Geda, Robert J Ivnik, Glenn E Smith, and Clifford R Jack. Mild cognitive impairment: ten years later. Archives of neurology, 66(12):1447–1455, 2009.Emily C Edmonds, Joel Eppig, Mark W Bondi, Kelly M Leyden, Bailey Goodwin, Lisa Delano-Wood, Carrie R McDonald, Alzheimer’s Disease Neuroimaging Initiative, et al. Heterogeneous cortical atrophy patterns in mci not captured by conventional diagnostic criteria. Neurology, 87(20):2108–2116, 2016.Emily C Edmonds, Lisa Delano-Wood, Lindsay R Clark, Amy J Jak, Daniel A Nation, Carrie R McDonald, David J Libon, Rhoda Au, Douglas Galasko, David P Salmon, et al. Susceptibility of the conventional criteria for mild cognitive impairment to false-positive diagnostic errors. Alzheimer’s & Dementia, 11(4):415–424, 2015.Tiffany F Hughes, Beth E Snitz, and Mary Ganguli. 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Mining relationships between multi-modal data to characterize Alzheimer’s disease manifestation

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