Alzheimer’s disease (AD) is a neurodegenerative disease characterized by memory loss and declined cognitive functioning. Brain changes in AD involve grey matter atrophy and changes in brain... Show moreAlzheimer’s disease (AD) is a neurodegenerative disease characterized by memory loss and declined cognitive functioning. Brain changes in AD involve grey matter atrophy and changes in brain function. These different brain characteristics can respectively be visualized with structural and functional MRI scans. These MRI modalities have been used for AD classification, but studies typically only include a limited number of features. In this thesis we derived multiple types of features from each MRI modality, and combined those to discriminate AD patients and elderly controls. First, we showed that AD classification accuracy increases when combining multiple types of measures from a single MRI modality. This was shown for structural MRI scans in chapter 2, and for resting state fMRI scans in chapter 3. In chapter 4 we evaluated whether MRI based AD classification models can discriminate AD in a diverse clinical population as well. This worked to some extent, and it worked best using structural MRI scans. In chapter 5 we used baseline multimodal MRI scans from the same diverse clinical population to predict two-year follow-up cognitive decline. Decline was predicted above chance level for the MMSE, but not for six other neuropsychological tests. Show less
Vos, F. de; Schouten, T.M.; Koini, M.; Bouts, M.J.R.J.; Feis, R.A.; Lechner, A.; ... ; Rombouts, S.A.R.B. 2020
Anatomical magnetic resonance imaging (MRI), diffusion MRI and resting state functional MRI (rs-fMRI) have been used for Alzheimer's disease (AD) classification. These scans are typically used to... Show moreAnatomical magnetic resonance imaging (MRI), diffusion MRI and resting state functional MRI (rs-fMRI) have been used for Alzheimer's disease (AD) classification. These scans are typically used to build models for discriminating AD patients from control subjects, but it is not clear if these models can also discriminate AD in diverse clinical populations as found in memory clinics.To study this, we trained MRI-based AD classification models on a single centre data set consisting of AD patients (N = 76) and controls (N = 173), and used these models to assign AD scores to subjective memory complainers (N = 67), mild cognitive impairment (MCI) patients (N = 61), and AD patients (N = 61) from a multi-centre memory clinic data set. The anatomical MRI scans were used to calculate grey matter density, subcortical volumes and cortical thickness, the diffusion MRI scans were used to calculate fractional anisotropy, mean, axial and radial diffusivity, and the rs-fMRI scans were used to calculate functional connectivity between resting state networks and amplitude of low frequency fluctuations. Within the multi-centre memory clinic data set we removed scan site differences prior to applying the models.For all models, on average, the AD patients were assigned the highest AD scores, followed by MCI patients, and later followed by SMC subjects. The anatomical MRI models performed best, and the best performing anatomical MRI measure was grey matter density, separating SMC subjects from MCI patients with an AUC of 0.69, MCI patients from AD patients with an AUC of 0.70, and SMC patients from AD patients with an AUC of 0.86. The diffusion MRI models did not generalise well to the memory clinic data, possibly because of large scan site differences. The functional connectivity model separated SMC subjects and MCI patients relatively good (AUC = 0.66). The multimodal MRI model did not improve upon the anatomical MRI model.In conclusion, we showed that the grey matter density model generalises best to memory clinic subjects. When also considering the fact that grey matter density generally performs well in AD classification studies, this feature is probably the best MRI-based feature for AD diagnosis in clinical practice. Show less
Green, C.; Sydow, A.; Vogel, S.; Anglada-Huguet, M.; Wiedermann, D.; Mandelkow, E.; ... ; Hoehn, M. 2019
