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BACKGROUND: FMRI resting state networks (RSNs) are used to characterize brain disorders. They also show extensive heterogeneity across patients. Identifying systematic differences between RSNs in patients, i.e. discovering neurofunctional subtypes, may further increase our understanding of disease heterogeneity. Currently, no methodology is available to estimate neurofunctional subtypes and their associated RSNs simultaneously. NEW METHOD: We present an unsupervised learning method for fMRI data, called Clusterwise Independent Component Analysis (C-ICA). This enables the clustering of patients into neurofunctional subtypes based on differences in shared ICA-derived RSNs. The parameters are estimated simultaneously, which leads to an improved estimation of subtypes and their associated RSNs. RESULTS: In five simulation studies, the C-ICA model is successfully validated using both artificially and realistically simulated data (N = 30-40). The successful performance of the C-ICA model is also illustrated on an empirical data set consisting of Alzheimer's disease patients and elderly control subjects (N = 250). C-ICA is able to uncover a meaningful clustering that partially matches (balanced accuracy = .72) the diagnostic labels and identifies differences in RSNs between the Alzheimer and control cluster. COMPARISON WITH OTHER METHODS: Both in the simulation study and the empirical application, C-ICA yields better results compared to competing clustering methods (i.e., a two step clustering procedure based on single subject ICA's and a Group ICA plus dual regression variant thereof) that do not simultaneously estimate a clustering and associated RSNs. Indeed, the overall mean adjusted Rand Index, a measure for cluster recovery, equals 0.65 for C-ICA and ranges from 0.27 to 0.46 for competing methods. CONCLUSIONS: The successful performance of C-ICA indicates that it is a promising method to extract neurofunctional subtypes from multi-subject resting state-fMRI data. This method can be applied on fMRI scans of patient groups to study (neurofunctional) subtypes, which may eventually further increase understanding of disease heterogeneity. ispartof: JOURNAL OF NEUROSCIENCE METHODS vol:382 ispartof: location:Netherlands status: published

Multi-view data refers to a setting where features are divided into feature sets, for example because they correspond to different sources. Stacked penalized logistic regression (StaPLR) is a recently introduced method that can be used for classification and automatically selecting the views that are most important for prediction. We introduce an extension of this method to a setting where the data has a hierarchical multi-view structure. We also introduce a new view importance measure for StaPLR, which allows us to compare the importance of views at any level of the hierarchy. We apply our extended StaPLR algorithm to Alzheimer's disease classification where different MRI measures have been calculated from three scan types: structural MRI, diffusion-weighted MRI, and resting-state fMRI. StaPLR can identify which scan types and which derived MRI measures are most important for classification, and it outperforms elastic net regression in classification performance.

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 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.

Inhibin-related proteins were identified in human follicular fluid following fractionation by gel permeation chromatography under neutral and acidic conditions, reversed-phase high performance liquid chromatography (HPLC) and preparative polyacrylamide gel electrophoresis. A number of molecular mass forms of inhibin (30-36 and 59-66 kDa) based on their in vitro biological and immunological activities were identified, of which 59-66 kDa inhibin was the predominant form. Bioactive fractions devoid of inhibin immunoactivity were also identified with molecular masses of 46 and 55 kDa. Based on their retention positions on reversed-phase HPLC and their lack of inhibin immunoactivity, these proteins are likely to be follicle stimulating hormone (FSH) suppressing proteins/follistatins previously identified in bovine and porcine follicular fluids. In addition, an immunoactive inhibin fraction devoid of bioactivity was identified in large amounts in the 50-70 kDa region of the gel permeation chromatogram at neutral pH. This material, based on previous findings with the fractionation of bovine follicular fluid, is likely to be the alpha-inhibin subunit precursor protein. No FSH stimulating activity (activin) was identified in any of the chromatograms, suggesting that the levels of activin in human follicular fluid are low. In conclusion, inhibins of molecular mass 30, 36, 59 and 66 kDa have been identified in human follicular fluid. Proteins with inhibin bioactivity devoid of immunoactivity and vice versa have also been detected and these proteins are probably FSH suppressing protein and an alpha-inhibin subunit precursor protein. Activin could not be identified.