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18 results on '"Bouts, Mark J.R.J."'

5. Big Data Approaches to Phenotyping Acute Ischemic Stroke Using Automated Lesion Segmentation of Multi-Center Magnetic Resonance Imaging Data

Author

Wu, Ona, Winzeck, Stefan, Giese, Anne-Katrin, Hancock, Brandon L., Etherton, Mark R., Bouts, Mark J.R.J., Donahue, Kathleen, Schirmer, Markus D., Irie, Robert E., Mocking, Steven J.T., McIntosh, Elissa C., Bezerra, Raquel, Kamnitsas, Konstantinos, Frid, Petrea, Wasselius, Johan, Cole, John W., Xu, Huichun, Holmegaard, Lukas, Jiménez-Conde, Jordi, Lemmens, Robin, Lorentzen, Eric, McArdle, Patrick F., Meschia, James F., Roquer, Jaume, Rundek, Tatjana, Sacco, Ralph L., Schmidt, Reinhold, Sharma, Pankaj, Slowik, Agnieszka, Stanne, Tara M., Thijs, Vincent, Vagal, Achala, Woo, Daniel, Bevan, Stephen, Kittner, Steven J., Mitchell, Braxton D., Rosand, Jonathan, Worrall, Bradford B., Jern, Christina, Lindgren, Arne G., Maguire, Jane, and Rost, Natalia S.

Published
2019
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6. Design and rationale for examining neuroimaging genetics in ischemic stroke: The MRI-GENIE study

Author

Giese, Anne-Katrin, Schirmer, Markus D., Donahue, Kathleen L., Cloonan, Lisa, Irie, Robert, Winzeck, Stefan, Bouts, Mark J.R.J., McIntosh, Elissa C., Mocking, Steven J., Dalca, Adrian V., Sridharan, Ramesh, Xu, Huichun, Frid, Petrea, Giralt-Steinhauer, Eva, Holmegaard, Lukas, Roquer, Jaume, Wasselius, Johan, Cole, John W., McArdle, Patrick F., Broderick, Joseph P., Jimenez-Conde, Jordi, Jern, Christina, Kissela, Brett M., Kleindorfer, Dawn O., Lemmens, Robin, Lindgren, Arne, Meschia, James F., Rundek, Tatjana, Sacco, Ralph L., Schmidt, Reinhold, Sharma, Pankaj, Slowik, Agnieszka, Thijs, Vincent, Woo, Daniel, Worrall, Bradford B., Kittner, Steven J., Mitchell, Braxton D., Rosand, Jonathan, Golland, Polina, Wu, Ona, and Rost, Natalia S.

Published
2017
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8. 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

12. 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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13. MRI-BASED CLASSIFICATION ACCURACY OF DEMENTIA TYPE IS DETERMINED BY MRI MODALITY

Author

Bouts, Mark J.R.J., Möller, Christiane, Hafkemeijer, Anne, van Swieten, John C., Dopper, Elise G.P., van der Flier, Wiesje M., Vrenken, Hugo, Wink, Alle Meije, Pijnenburg, Yolande A.L., Scheltens, Philip, Barkhof, Frederik, van der Grond, Jeroen, de Rooij, Mark, Rombouts, Serge A.R.B., Psychiatry, Amsterdam Neuroscience - Neurodegeneration, CCA - Imaging and biomarkers, Human genetics, Neurology, APH - Personalized Medicine, APH - Methodology, Radiology and nuclear medicine, Amsterdam Neuroscience - Brain Imaging, Amsterdam Neuroscience - Neuroinfection & -inflammation, Divisions, Amsterdam Neuroscience - Cellular & Molecular Mechanisms, CCA - Cancer biology and immunology, and Physics and medical technology

Abstract

Background: Multimodal MRI may support single-subject differentiation diagnosis of Alzheimer's disease (AD) and behavioral variant frontotemporal dementia (bvFTD). Yet, what MRI modality combinations maximize classification accuracy is not yet known. We analyzed various MRI modality combinations to determine what combinations suit single-subject classification of AD patients, bvFTD patients, and control subjects best. Methods: Thirty-seven patients with probable AD, 28 patients with bvFTD, and 40 cognitively normal subjects (Controls) (Table 1) underwent diffusion tensor imaging (DTI), resting-state functional, and T1-weighted structural MRI at 3T. From each T1-w image, probabilistic gray matter (GM) maps were calculated and used to estimate local GM density (GMD) within 96 Harvard-Oxford cortical and 14 deep GM regions. 20 tracts of the Johns-Hopkins-University tractography atlas were used to determine local white matter density (WMD) values and, after projection on the study-specific skeleton calculated with tract-based spatial statistics, local measures of fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity, and radial diffusivity. Seventy rs-fMRI components, derived from a temporally concatenated independent component analysis, were used to calculate the components' pairwise correlations and partial correlations. These features were used to establish classification accuracy using nested cross-validation classification analyses with elastic net classifiers. Individual and combinations of MRI modalities were step-wise evaluated to determine classification accuracy, expressed as area under the receiver operating characteristic curves (AUC). A three-group classification model was used to determine the accuracy for differentiating between AD, bvFTD, and Control subjects. Three two-group models were calculated to differentiate AD from Controls, bvFTD from Controls, and AD from bvFTD subjects. Results: Classification accuracy for three-group and AD versus Control classifications were highest with GMD-based features ((AUC [min-max]) 0.7±5[0.711-0.847] and 0.933[0.886- 0.963]) (Table 2). bvFTD versus Control and AD versus bvFTD classification models were most accurate using DTI-based features (FA: 0.860[0.807-0.913], MD: 0.693[0.614-0.821]). Overall, rsfMRI features did not improve classification accuracy. Conclusions: MRI-based differentiation of dementia forms may depend on dementia- type and MRI modality. T1-w-based measures particularly supported AD classifications, whereas DTI-based measurements facilitated bvFTD classifications. Functional connectivity measures and multimodal combinations did not substantially improve dementia- type discrimination. For MRI-based differential dementiatype diagnosis careful selection of MRI modalities is warranted.

Published
2017
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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. [P2-338]: ARE NEUROFILAMENT LIGHT CHAIN AND WHITE MATTER INTEGRITY RELATED BIOMARKERS FOR FAMILIAL FRONTOTEMPORAL DEMENTIA?

Author

Panman, Jessica L., primary, Louise van der Ende, Emma, additional, Meeter, Lieke H.H., additional, Bouts, Mark J.R.J., additional, Dopper, Elise G.P., additional, Jiskoot, Lize C., additional, Kaat, Laura Donker, additional, van Minkelen, Rick, additional, Teunissen, Charlotte E., additional, Rombouts, Serge A.R.B., additional, van Swieten, John C., additional, and Papma, Janne M., additional

Published
2017
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16. [IC-P-130]: MRI-BASED CLASSIFICATION ACCURACY OF DEMENTIA TYPE IS DETERMINED BY MRI MODALITY

Author

Bouts, Mark J.R.J., primary, Möller, Christiane, additional, Hafkemeijer, Anne, additional, van Swieten, John C., additional, Dopper, Elise G.P., 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, van der Grond, Jeroen, additional, de Rooij, Mark, additional, and Rombouts, Serge A.R.B., additional

Published
2017
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17. 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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