Tracking pathophysiological process in Alzheimer’s disease through structural and functional brain topological patterns

The main aim ofThe main aim of this PhD thesis is to create a predictive model of AD progression using a neuroimaging multimodal approach and brain network analysis in an effort to link cognitive, genetic and physiological factors with structural and/or functional brain topological patterns. Being able to identify abnormal topological patterns that may constitute an early precursor of AD will comprise the MCI group to one clinical entity. Therefore, we expected an increase in the predictive validity of MCI brain topological patterns as a predementia syndrome and/or as biomarker of AD pathology.

The project constitutes three interdependent subprojects, which allow for independent analysis within the subprojects as well as data-fusion analyses gathering genetic, neuropsychological, physiological, structural and functional data together. The data samples for these subprojects will be selected from different Alzheimer Disease databases. The first subproject deals with the early detection of aberrant topological brain patterns during AD progression and aims at characterizing the structural and functional brain network patterns in MCI converters to AD and the interaction between brain network features and neuropsychological variables. The second subproject describes the association between brain topological patterns in MCI converters to AD and genetic risk factors and comorbidity in comparison to non-converters. Finally the third subproject builds and tests a predictive model for clinical AD progression using advanced statistical multivariate methods and machine learning techniques.

this PhD thesis is to create a predictive model of AD progression using a neuroimaging multimodal approach and brain network analysis in an effort to link cognitive, genetic and physiological factors with structural and/or functional brain topological patterns. Being able to identify abnormal topological patterns that may constitute an early precursor of AD will comprise the MCI group to one clinical entity. Therefore, we expected an increase in the predictive validity of MCI brain topological patterns as a predementia syndrome and/or as biomarker of AD pathology.

The project constitutes three interdependent subprojects, which allow for independent analysis within the subprojects as well as data-fusion analyses gathering genetic, neuropsychological, physiological, structural and functional data together. The data samples for these subprojects will be selected from different Alzheimer Disease databases. The first subproject deals with the early detection of aberrant topological brain patterns during AD progression and aims at characterizing the structural and functional brain network patterns in MCI converters to AD and the interaction between brain network features and neuropsychological variables. The second subproject describes the association between brain topological patterns in MCI converters to AD and genetic risk factors and comorbidity in comparison to non-converters. Finally the third subproject builds and tests a predictive model for clinical AD progression using advanced statistical multivariate methods and machine learning techniques.