Computational anatomy strategies for characterization of brain patterns associated with Alzheimer's disease

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dc.contributor.advisorRomero Castro, Eduardo
dc.contributor.advisorSijbers, Jan
dc.contributor.advisorJeurissen, Ben
dc.contributor.authorGiraldo Franco, Diana Lorena
dc.contributor.researchgroupCim@Labspa
dc.date.accessioned2022-03-28T17:54:01Z
dc.date.available2022-03-28T17:54:01Z
dc.date.issued2022
dc.descriptionilustraciones, fotografías, graficasspa
dc.description.abstractLa enfermedad de Alzheimer (EA) es una de las fallas sistemáticas del sistema nervioso más complejas que se conocen. Los síntomas clínicos de esta enfermedad neurodegenerativa son alteraciones de la cognición y el comportamiento que pueden conducir a la aparición de un síndrome de demencia. Los mecanismos de la enfermedad que conducen a la neurodegeneración y al deterioro cognitivo en la EA aún no se conocen bien, lo que dificulta la predicción de la evolución clínica de los pacientes en las primeras fases de la EA. Actualmente, ningún biomarcador o examen es suficiente para diagnosticar la EA y los instrumentos estándar existentes no son lo suficientemente sensibles para detectar cambios sutiles, predecir el curso clínico o reconocer presentaciones atípicas de EA. Esta tesis presenta dos estrategias de anatomía computacional destinadas a identificar y cuantificar los patrones de neurodegeneración asociados a diferentes etapas clínicas a lo largo del continuo de la EA utilizando dos modalidades diferentes de imágenes de resonancia magnética. Una tercera contribución consiste en una estrategia guiada por datos para desarrollar un conjunto de puntajes específicas por dominio que resultan útiles para estimar el riesgo y predecir la progresión del deterioro cognitivo leve a la demencia. La evaluación de estas estrategias con métodos de aprendizaje automático y de inferencia estadística demuestra el potencial de las herramientas cuantitativas propuestas para ayudar al manejo y el seguimiento clínico de los pacientes y podría utilizarse para mejorar la evaluación de posibles intervenciones que puedan modificar el curso de la enfermedad. (Texto tomado de la fuente)spa
dc.description.abstractAlzheimer's disease (AD) is one of the most complex systematic malfunctions of the nervous system that are known. The clinical symptoms of this neurodegenerative disease are alterations in cognition and behaviour that can lead to the onset of a dementia syndrome. Disease mechanisms that lead to neurodegeneration and cognitive impairment in sporadic AD are not well understood yet, making it difficult to predict the clinical progression of patients at the early stages of the AD continuum. Currently, no single biomarker or exam is sufficient to diagnose AD and existing standard instruments are not sensitive enough to detect subtle changes, predict the clinical course, and recognize heterogeneous forms of AD. This thesis presents two computational anatomy strategies aiming to identify and quantify neurodegeneration patterns associated with different clinical stages along the AD continuum using two different modalities of magnetic resonance imaging. A third contribution consists of a data-driven strategy to develop a set of domain-specific scores that result useful to estimate the risk of and predict the progression from mild cognitive impairment to dementia. Evaluation of these strategies with machine-learning and statistical inference methods demonstrate the potential of the proposed quantitative tools to help patients' clinical management and monitoring and could be used to improve the evaluation of potential disease-modifying interventions.eng
dc.description.abstractDe ziekte van Alzheimer (AD) is een van de meest complexe systemische storingen van het zenuwstelsel die bekend zijn. De klinische symptomen van deze neurodegeneratieve ziekte zijn veranderingen in cognitie en gedrag die kunnen leiden tot het ontstaan van een dementiesyndroom. De ziektemechanismen die leiden tot neurodegeneratie en cognitieve stoornissen bij sporadische AD zijn nog niet goed begrepen, waardoor het moeilijk is om de klinische progressie van patiënten in de vroege stadia van het AD continuüm te voorspellen. Momenteel is geen enkele biomarker of onderzoek voldoende om de diagnose AD te stellen en de bestaande standaardinstrumenten zijn niet gevoelig genoeg om subtiele veranderingen te detecteren, het klinische verloop te voorspellen en heterogene vormen van AD te herkennen. Dit proefschrift presenteert twee computationele anatomiestrategieën die gericht zijn op het identificeren en kwantificeren van neurodegeneratiepatronen geassocieerd met verschillende klinische stadia in het AD continuüm, gebruikmakend van twee verschillende modaliteiten van magnetische resonantie beeldvorming. Een derde bijdrage bestaat uit een data-gestuurde strategie om een reeks van domeinspecifieke scores te ontwikkelen die bruikbaar zijn om het risico in te schatten op en de progressie te voorspellen van milde cognitieve stoornissen naar dementie. Evaluatie van deze strategieën met machine-learning en statistische inferentie methoden tonen het potentieel aan van de voorgestelde kwantitatieve instrumenten om het klinisch management en de monitoring van patiënten te helpen en zouden gebruikt kunnen worden om de evaluatie van potentiële ziekte-modificerende interventies te verbeteren.other
dc.description.degreelevelDoctoradospa
dc.description.degreenameDoctor en Ingenieríaspa
dc.format.extentxvii, 81 páginasspa
dc.format.mimetypeapplication/pdfspa
dc.identifier.instnameUniversidad Nacional de Colombiaspa
dc.identifier.reponameRepositorio Institucional Universidad Nacional de Colombiaspa
dc.identifier.repourlhttps://repositorio.unal.edu.co/spa
dc.identifier.urihttps://repositorio.unal.edu.co/handle/unal/81406
dc.language.isoengspa
dc.publisherUniversidad Nacional de Colombiaspa
dc.publisherUniversiteit Antwerpenspa
dc.publisher.branchUniversidad Nacional de Colombia - Sede Bogotáspa
dc.publisher.departmentDepartamento de Ingeniería de Sistemas e Industrialspa
dc.publisher.facultyFacultad de Ingenieríaspa
dc.publisher.placeBogotá, Colombiaspa
dc.publisher.programBogotá - Ingeniería - Doctorado en Ingeniería - Sistemas y Computaciónspa
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dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.rights.licenseAtribución-NoComercial-CompartirIgual 4.0 Internacionalspa
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/spa
dc.subject.ddc000 - Ciencias de la computación, información y obras generales::005 - Programación, programas, datos de computaciónspa
dc.subject.ddc620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingenieríaspa
dc.subject.otherEnfermedad de Alzheimerspa
dc.subject.otherAlzheimer Diseaseeng
dc.subject.otherDiagnóstico por Imagenspa
dc.subject.otherDiagnostic Imagingeng
dc.subject.proposalNeuroimagingeng
dc.subject.proposalMedical image processingeng
dc.subject.proposalMagnetic resonance imagingeng
dc.subject.proposalCognitive impairmenteng
dc.subject.proposalNeuroimágenesspa
dc.subject.proposalProcesamiento de imágenes médicasspa
dc.subject.proposalImágenes de resonancia magnéticaspa
dc.subject.proposalDeterioro cognitivospa
dc.subject.proposalZiekte van Alzheimerother
dc.subject.proposalNeurobeeldvormingother
dc.subject.proposalMedische Beeldverwerkingother
dc.subject.proposalMagnetische Resonantie Beeldvormingother
dc.subject.proposalCognitieve Stoornisother
dc.subject.unescoMedicina preventivaspa
dc.subject.unescoPreventive medicineeng
dc.titleComputational anatomy strategies for characterization of brain patterns associated with Alzheimer's diseaseeng
dc.title.translatedEstrategias de anatomía computacional para la caracterización de patrones cerebrales asociados a la enfermedad de Alzheimerspa
dc.title.translatedComputationele anatomie strategieën voor karakterisering van hersenpatronen geassocieerd met de ziekte van Alzheimerother
dc.typeTrabajo de grado - Doctoradospa
dc.type.coarhttp://purl.org/coar/resource_type/c_db06spa
dc.type.coarversionhttp://purl.org/coar/version/c_ab4af688f83e57aaspa
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dc.type.driverinfo:eu-repo/semantics/doctoralThesisspa
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dc.type.versioninfo:eu-repo/semantics/acceptedVersionspa
dcterms.audience.professionaldevelopmentEstudiantesspa
dcterms.audience.professionaldevelopmentInvestigadoresspa
dcterms.audience.professionaldevelopmentMaestrosspa
dcterms.audience.professionaldevelopmentPúblico generalspa
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oaire.awardtitleConvocatoria 727 de 2015spa
oaire.fundernameMinCienciasspa

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