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... Show moreMulti-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. Show less
Feis, R.A.; Grond, J. van der; Bouts, M.J.R.J.; Panman, J.L.; Poos, J.M.; Schouten, T.M.; ... ; Rombouts, S.A.R.B. 2020
Frontotemporal dementia is a highly heritable and devastating neurodegenerative disease. About 10-20% of all frontotemporal dementia is caused by known pathogenic mutations, but a reliable tool to... Show moreFrontotemporal dementia is a highly heritable and devastating neurodegenerative disease. About 10-20% of all frontotemporal dementia is caused by known pathogenic mutations, but a reliable tool to predict clinical conversion in mutation carriers is lacking. In this retrospective proof-of-concept case-control study, we investigate whether MRI-based and cognition-based classifiers can predict which mutation carriers from genetic frontotemporal dementia families will develop symptoms ('convert') within 4 years. From genetic frontotemporal dementia families, we included 42 presymptomatic frontotemporal dementia mutation carriers. We acquired anatomical, diffusion-weighted imaging, and resting-state functional MRI, as well as neuropsychological data. After 4 years, seven mutation carriers had converted to frontotemporal dementia ('converters'), while 35 had not ('non-converters'). We trained regularized logistic regression models on baseline MRI and cognitive data to predict conversion to frontotemporal dementia within 4 years, and quantified prediction performance using area under the receiver operating characteristic curves. The prediction model based on fractional anisotropy, with highest contribution of the forceps minor, predicted conversion to frontotemporal dementia beyond chance level (0.81 area under the curve, family-wise error corrected P = 0.025 versus chance level). Other MRI-based and cognitive features did not outperform chance level. Even in a small sample, fractional anisotropy predicted conversion in presymptomatic frontotemporal dementia mutation carriers beyond chance level. After validation in larger data sets, conversion prediction in genetic frontotemporal dementia may facilitate early recruitment into clinical trials. Show less
Durieux, J.; Rombouts, S.A.R.B.; Vos, F. de; Koini, M.; Wilderjans, T.F. 2022
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... Show moreBackground: 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 neuro-functional 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. Show less
Durieux, J.; Rombouts, S.A.R.B.; Vos, F. de; Koini, M.; Wilderjans, T.F. 2022
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... Show moreBackground: 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 neuro-functional 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. Show less
Schouten, T.M.; Koini, M.; Vos, F. de; Seiler, S.; Van der Grond, J.; Lechner, A.; ... ; Rombouts, S.A.R.B. 2016
Magnetic resonance imaging (MRI) is sensitive to structural and functional changes in the brain caused by Alzheimer's disease (AD), and can therefore be used to help in diagnosing the disease.... Show moreMagnetic resonance imaging (MRI) is sensitive to structural and functional changes in the brain caused by Alzheimer's disease (AD), and can therefore be used to help in diagnosing the disease. Improving classification of AD patients based on MRI scans might help to identify AD earlier in the disease's progress, which may be key in developing treatments for AD. In this study we used an elastic net classifier based on several measures derived from the MRI scans of mild to moderate AD patients (N = 77) from the prospective registry on dementia study and controls (N = 173) from the Austrian Stroke Prevention Family Study. We based our classification on measures from anatomical MRI, diffusion weighted MRI and resting state functional MRI. Our unimodal classification performance ranged from an area under the curve (AUC) of 0.760 (full correlations between functional networks) to 0.909 (grey matter density). When combining measures from multiple modalities in a stepwise manner, the classification performance improved to an AUC of 0.952. This optimal combination consisted of grey matter density, white matter density, fractional anisotropy, mean diffusivity, and sparse partial correlations between functional networks. Classification performance for mild AD as well as moderate AD also improved when using this multimodal combination. We conclude that different MRI modalities provide complementary information for classifying AD. Moreover, combining multiple modalities can substantially improve classification performance over unimodal classification. (C) 2016 The Authors. Published by Elsevier Inc. Show less
Vos, F. de; Schouten, T.M.; Hafkemeijer, A.; Dopper, E.G.P.; Swieten, J.C. van; Rooij, M. de; ... ; Rombouts, S.A.R.B. 2016
Several anatomical MRI markers for Alzheimer's disease (AD) have been identified. Hippocampal volume, cortical thickness, and grey matter density have been used successfully to discriminate AD... Show moreSeveral anatomical MRI markers for Alzheimer's disease (AD) have been identified. Hippocampal volume, cortical thickness, and grey matter density have been used successfully to discriminate AD patients from controls. These anatomical MRI measures have so far mainly been used separately. The full potential of anatomical MRI scans for AD diagnosis might thus not yet have been used optimally. In this study, we therefore combined multiple anatomical MRI measures to improve diagnostic classification of AD. For 21 clinically diagnosed AD patients and 21 cognitively normal controls, we calculated (i) cortical thickness, (ii) cortical area, (iii) cortical curvature, (iv) grey matter density, (v) subcortical volumes, and (vi) hippocampal shape. These six measures were used separately and combined as predictors in an elastic net logistic regression. We made receiver operating curve plots and calculated the area under the curve (AUC) to determine classification performance. AUC values for the single measures ranged from 0.67 (cortical thickness) to 0.94 (grey matter density). The combination of all six measures resulted in an AUC of 0.98. Our results demonstrate that the different anatomical MRI measures contain complementary information. A combination of these measures may therefore improve accuracy of AD diagnosis in clinical practice. Hum Brain Mapp 37:1920-1929, 2016. (c) 2016 Wiley Periodicals, Inc. Show less
Vos, F. de; Schouten, T.M.; Hafkemeijer, A.; Dopper, E.G.; Swieten, J.C. van; Rooij, M. de; ... ; Rombouts, S.A. 2016
Alzheimer's disease (AD) patients show altered patterns of functional connectivity (FC) on resting state functional magnetic resonance imaging (RSfMRI) scans. It is yet unclear which RSfMRI... Show moreAlzheimer's disease (AD) patients show altered patterns of functional connectivity (FC) on resting state functional magnetic resonance imaging (RSfMRI) scans. It is yet unclear which RSfMRI measures are most informative for the individual classification of AD patients. We investigated this using RSfMRI scans from 77 AD patients (MMSE = 20.4 ± 4.5) and 173 controls (MMSE = 27.5 ± 1.8). We calculated i) FC matrices between resting state components as obtained with independent component analysis (ICA), ii) the dynamics of these FC matrices using a sliding window approach, iii) the graph properties (e.g., connection degree, and clustering coefficient) of the FC matrices, and iv) we distinguished five FC states and administered how long each subject resided in each of these five states. Furthermore, for each voxel we calculated v) FC with 10 resting state networks using dual regression, vi) FC with the hippocampus, vii) eigenvector centrality, and viii) the amplitude of low frequency fluctuations (ALFF). These eight measures were used separately as predictors in an elastic net logistic regression, and combined in a group lasso logistic regression model. We calculated the area under the receiver operating characteristic curve plots (AUC) to determine classification performance. The AUC values ranged between 0.51 and 0.84 and the highest were found for the FC matrices (0.82), FC dynamics (0.84) and ALFF (0.82). The combination of all measures resulted in an AUC of 0.85. We show that it is possible to obtain moderate to good AD classification using RSfMRI scans. FC matrices, FC dynamics and ALFF are most discriminative and the combination of all the resting state measures improves classification accuracy slightly. Show less
Meer, A.F. van; Vos, F. de; Hermans, R.C.J.; Peeters, P.A.; Dillen, L.F. van 2022
The rapidly increasing prevalence of overweight and obesity has heightened the need for a better understanding of obesity-related eating patterns and dietary behaviours. Recent work suggests that... Show moreThe rapidly increasing prevalence of overweight and obesity has heightened the need for a better understanding of obesity-related eating patterns and dietary behaviours. Recent work suggests that distracted eating is causally related to increased immediate and later food, pushing the need for a better understanding of the prevalence of distracted consumption and how this relates to body weight. To extract insights in the relationship between demographics, daily consumption settings, and BMI, we performed secondary data analyses on data from 1011 individuals representative of the Dutch population (adults, 507F, BMI 17–50 kg/m2). The most commonly reported distractions were talking to others (32.7%) and watching television (21.7%). Only 18.4% of respondents reported no distractions during meals. To examine how different distractions related to BMI, we performed OLS regression which showed, among other things, that watching tv while eating lunch (η2 = 0.37) and working during dinner were associated with a higher BMI (η2 = 1.63). To examine the robustness of these findings, machine learning techniques were used. A random forest analysis (RMSE = 4.09) showed that next to age and education level, distraction during lunch and snack was amongst the largest predictors of BMI. Multiple linear regression with lasso penalty (RMSE = 4.13) showed that specifically watching tv while eating lunch or snacks was associated with a higher BMI. In conclusion, our analyses confirmed the assumption that people are regularly distracted during their daily meals, with distinct distractors relating to BMI. These findings provide a starting point for evidence-based recommendations on which consumption settings are associated with healthier eating patterns and body weight. Show less
Bouts, M.J.R.J.; Grond, J. van der; Vernooij, M.W.; Koini, M.; Schouten, T.M.; Vos, F. de; ... ; Rombouts, S.A.R.B. 2019
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
