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16 results on '"Feis, Rogier A."'

7. Pre-trained MRI-based Alzheimer's disease classification models to classify memory clinic patients

Author

Vos, Frank de, Schouten, T., Koini, M., Bouts, Mark J.R.J., Feis, Rogier A., Lechner, Anita, Olde Rikkert, M.G.M., Grond, Jeroen van der, Rombouts, S.A.R.B., Vos, Frank de, Schouten, T., Koini, M., Bouts, Mark J.R.J., Feis, Rogier A., Lechner, Anita, Olde Rikkert, M.G.M., Grond, Jeroen van der, and Rombouts, S.A.R.B.

Abstract

Contains fulltext : 222165.pdf (publisher's version ) (Open Access)

Published
2020

8. Erratum: Single-subject classification of presymptomatic frontotemporal dementia mutation carriers using multimodal MRI (NeuroImage: Clinical (2018) 20 (188–196), (S2213158218302262), (10.1016/j.nicl.2018.07.014))

Author

Feis, Rogier A., Bouts, Mark J. R. J., Panman, Jessica L., Jiskoot, Lize C., Dopper, Elise G. P., Schouten, Tijn M., de Vos, Frank, van der Grond, Jeroen, van Swieten, John C., and Rombouts, Serge A. R. B.

Subjects
mental disorders
Abstract

Background: Classification models based on magnetic resonance imaging (MRI) may aid early diagnosis of frontotemporal dementia (FTD) but have only been applied in established FTD cases. Detection of FTD patients in earlier disease stages, such as presymptomatic mutation carriers, may further advance early diagnosis and treatment. In this study, we aim to distinguish presymptomatic FTD mutation carriers from controls on an individual level using multimodal MRI-based classification. Methods: Anatomical MRI, diffusion tensor imaging (DTI) and resting-state functional MRI data were collected in 55 presymptomatic FTD mutation carriers (8 microtubule-associated protein Tau, 35 progranulin, and 12 chromosome 9 open reading frame 72) and 48 familial controls. We calculated grey and white matter density features from anatomical MRI scans, diffusivity features from DTI, and functional connectivity features from resting-state functional MRI. These features were applied in a recently introduced multimodal behavioural variant FTD (bvFTD) classification model, and were subsequently used to train and test unimodal and multimodal carrier-control models. Classification performance was quantified using area under the receiver operator characteristic curves (AUC). Results: The bvFTD model was not able to separate presymptomatic carriers from controls beyond chance level (AUC = 0.582, p = 0.078). In contrast, one unimodal and several multimodal carrier-control models performed significantly better than chance level. The unimodal model included the radial diffusivity feature and had an AUC of 0.642 (p = 0.032). The best multimodal model combined radial diffusivity and white matter density features (AUC = 0.684, p = 0.004). Conclusions: FTD mutation carriers can be separated from controls with a modest AUC even before symptom-onset, using a newly created carrier-control classification model, while this was not possible using a recent bvFTD classification model. A multimodal MRI-based classification score may therefore be a useful biomarker to aid earlier FTD diagnosis. The exclusive selection of white matter features in the best performing model suggests that the earliest FTD-related pathological processes occur in white matter.

Published
2019
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9. Classification using fractional anisotropy predicts conversion in genetic frontotemporal dementia, a proof of concept

Author

Feis, Rogier A, primary, van der Grond, Jeroen, additional, Bouts, Mark J R J, additional, Panman, Jessica L, additional, Poos, Jackie M, additional, Schouten, Tijn M, additional, de Vos, Frank, additional, Jiskoot, Lize C, additional, Dopper, Elise G P, additional, van Buchem, Mark A, additional, van Swieten, John C, additional, and Rombouts, Serge A R B, additional

Published
2020
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11. Detection of mild cognitive impairment in a community‐dwelling population using quantitative, multiparametric MRI‐based classification

Author

Bouts, Mark J. R. J., primary, Grond, Jeroen, additional, Vernooij, Meike W., additional, Koini, Marisa, additional, Schouten, Tijn M., additional, Vos, Frank, additional, Feis, Rogier A., additional, Cremers, Lotte G. M., additional, Lechner, Anita, additional, Schmidt, Reinhold, additional, Rooij, Mark, additional, Niessen, Wiro J., additional, Ikram, M. Arfan, additional, and Rombouts, Serge A. R. B., additional

Published
2019
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12. Multiple Approaches to Diffusion Magnetic Resonance Imaging in Hereditary Cerebral Amyloid Angiopathy Mutation Carriers

Author

Schouten, Tijn M., primary, de Vos, Frank, additional, van Rooden, Sanneke, additional, Bouts, Mark J. R. J., additional, van Opstal, Anna M., additional, Feis, Rogier A., additional, Terwindt, Gisela M., additional, Wermer, Marieke J. H., additional, van Buchem, Mark A., additional, Greenberg, Steven M., additional, de Rooij, Mark, additional, Rombouts, Serge A. R. B., additional, and van der Grond, Jeroen, additional

Published
2019
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13. Detection of mild cognitive impairment in a community-dwelling population using quantitative, multiparametric MRI-based classification

Author

Bouts, Mark J.R.J. (author), van der Grond, Jeroen (author), Vernooij, Meike W. (author), Koini, Marisa (author), Schouten, T.M. (author), de Vos, Frank (author), Feis, Rogier A. (author), Lechner, Anita (author), Niessen, W.J. (author), Bouts, Mark J.R.J. (author), van der Grond, Jeroen (author), Vernooij, Meike W. (author), Koini, Marisa (author), Schouten, T.M. (author), de Vos, Frank (author), Feis, Rogier A. (author), Lechner, Anita (author), and Niessen, W.J. (author)

Abstract

Early and accurate mild cognitive impairment (MCI) detection within a heterogeneous, nonclinical population is needed to improve care for persons at risk of developing dementia. Magnetic resonance imaging (MRI)-based classification may aid early diagnosis of MCI, but has only been applied within clinical cohorts. We aimed to determine the generalizability of MRI-based classification probability scores to detect MCI on an individual basis within a general population. To determine classification probability scores, an AD, mild-AD, and moderate-AD detection model were created with anatomical and diffusion MRI measures calculated from a clinical Alzheimer's Disease (AD) cohort and subsequently applied to a population-based cohort with 48 MCI and 617 normal aging subjects. Each model's ability to detect MCI was quantified using area under the receiver operating characteristic curve (AUC) and compared with an MCI detection model trained and applied to the population-based cohort. The AD-model and mild-AD identified MCI from controls better than chance level (AUC = 0.600, p = 0.025; AUC = 0.619, p = 0.008). In contrast, the moderate-AD-model was not able to separate MCI from normal aging (AUC = 0.567, p = 0.147). The MCI-model was able to separate MCI from controls better than chance (p = 0.014) with mean AUC values comparable with the AD-model (AUC = 0.611, p = 1.0). Within our population-based cohort, classification models detected MCI better than chance. Nevertheless, classification performance rates were moderate and may be insufficient to facilitate robust MRI-based MCI detection on an individual basis. Our data indicate that multiparametric MRI-based classification algorithms, that are effective in clinical cohorts, may not straightforwardly translate to applications in a general population., YES! Delft, ImPhys/Quantitative Imaging

Published
2019
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14. Single Subject Classification of Alzheimer’s Disease and Behavioral Variant Frontotemporal Dementia Using Anatomical, Diffusion Tensor, and Resting-State Functional Magnetic Resonance Imaging

Author

Bouts, Mark J.R.J., primary, Möller, Christiane, additional, Hafkemeijer, Anne, additional, van Swieten, John C., additional, Dopper, Elise, additional, van der Flier, Wiesje M., additional, Vrenken, Hugo, additional, Wink, Alle Meije, additional, Pijnenburg, Yolande A.L., additional, Scheltens, Philip, additional, Barkhof, Frederik, additional, Schouten, Tijn. M., additional, de Vos, Frank, additional, Feis, Rogier A., additional, van der Grond, Jeroen, additional, de Rooij, Mark, additional, and Rombouts, Serge A.R.B., additional

Published
2018
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15. Single Subject Classification of Alzheimer's Disease and Behavioral Variant Frontotemporal Dementia Using Anatomical, Diffusion Tensor, and Resting-State Functional Magnetic Resonance Imaging.

Author

Zhang, Bouts, Mark J.R.J., Möller, Christiane, Hafkemeijer, Anne, Schouten, Tijn. M., de Vos, Frank, Rombouts, Serge A.R.B., de Rooij, Mark, van der Grond, Jeroen, Feis, Rogier A., van Swieten, John C., Dopper, Elise, Pijnenburg, Yolande A.L., Scheltens, Philip, van der Flier, Wiesje M., Wink, Alle Meije, Barkhof, Frederik, and Vrenken, Hugo

Subjects
ALZHEIMER'S disease, FRONTOTEMPORAL dementia, MAGNETIC resonance imaging of the brain, DIFFERENTIAL diagnosis, DIFFUSION tensor imaging, BRAIN, COMPARATIVE studies, DIAGNOSTIC imaging, MAGNETIC resonance imaging, RESEARCH methodology, MEDICAL cooperation, COMPUTERS in medicine, PHARMACOKINETICS, RELAXATION for health, RESEARCH, EVALUATION research, RETROSPECTIVE studies, RECEIVER operating characteristic curves
Abstract

Background/objective: Overlapping clinical symptoms often complicate differential diagnosis between patients with Alzheimer's disease (AD) and behavioral variant frontotemporal dementia (bvFTD). Magnetic resonance imaging (MRI) reveals disease specific structural and functional differences that aid in differentiating AD from bvFTD patients. However, the benefit of combining structural and functional connectivity measures to-on a subject-basis-differentiate these dementia-types is not yet known.Methods: Anatomical, diffusion tensor (DTI), and resting-state functional MRI (rs-fMRI) of 30 patients with early stage AD, 23 with bvFTD, and 35 control subjects were collected and used to calculate measures of structural and functional tissue status. All measures were used separately or selectively combined as predictors for training an elastic net regression classifier. Each classifier's ability to accurately distinguish dementia-types was quantified by calculating the area under the receiver operating characteristic curves (AUC).Results: Highest AUC values for AD and bvFTD discrimination were obtained when mean diffusivity, full correlations between rs-fMRI-derived independent components, and fractional anisotropy (FA) were combined (0.811). Similarly, combining gray matter density (GMD), FA, and rs-fMRI correlations resulted in highest AUC of 0.922 for control and bvFTD classifications. This, however, was not observed for control and AD differentiations. Classifications with GMD (0.940) and a GMD and DTI combination (0.941) resulted in similar AUC values (p = 0.41).Conclusion: Combining functional and structural connectivity measures improve dementia-type differentiations and may contribute to more accurate and substantiated differential diagnosis of AD and bvFTD patients. Imaging protocols for differential diagnosis may benefit from also including DTI and rs-fMRI. [ABSTRACT FROM AUTHOR]

Published
2018
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16. ICA-based artifact removal diminishes scan site differences in multi-center resting-state fMRI

Author

Feis, Rogier A., primary, Smith, Stephen M., additional, Filippini, Nicola, additional, Douaud, Gwenaëlle, additional, Dopper, Elise G. P., additional, Heise, Verena, additional, Trachtenberg, Aaron J., additional, van Swieten, John C., additional, van Buchem, Mark A., additional, Rombouts, Serge A. R. B., additional, and Mackay, Clare E., additional

Published
2015
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