Fabian Wagner
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Research Interests
Fabian Wagner has a total of 8 co-authors. Here are the top co-authors by number of shared publications:
- Benno Gesierich
Medical Image Analysis Center (MIAC), Basel, Switzerland
1 shared publication - Christian Enzinger
Department of Neurology, Medical University of Graz, Graz, Austria
1 shared publication - Florian J. Mayer
Department of Laboratory Medicine, Division of Clinical Virology, Medical University of Vienna, Vienna, Austria
1 shared publication - Marco Duering
Institute for Stroke and Dementia Research, Ludwig-Maximilians Universität, Munich, Germany (A.K., M.D.).
1 shared publication - Marisa Koini
Department of Neurology, Medical University of Graz, Graz, Austria
1 shared publication Peter Dal-Bianco
Department of Neurology, Medical University of Vienna, Vienna, Austria
1 shared publication
Latest Publications
- Open Access
- 5 May 2020
- Published by Frontiers Media SA in Frontiers in Psychiatry
- Vol. 11, 360
Gray Matter Covariance Networks as Classifiers and Predictors of Cognitive Function in Alzheimer’s Disease
The study of shared variation in gray matter morphology may define neurodegenerative diseases beyond what can be detected from the isolated assessment of regional brain volumes. We, therefore, aimed to (1) identify SCNs (structural covariance networks) that discriminate between Alzheimer’s disease (AD) patients and healthy controls (HC), (2) investigate their diagnostic accuracy in comparison and above established markers, and (3) determine if they are associated with cognitive abilities. We applied a random forest algorithm to identify discriminating networks from a set of 20 SCNs. The algorithm was trained on a main sample of 104 AD patients and 104 age-matched HC and was then validated in an independent sample of 28 AD patients and 28 controls from another center. Only two of the 20 SCNs contributed significantly to the discrimination between AD and controls. These were a temporal and a secondary somatosensory SCN. Their diagnostic accuracy was 74% in the original cohort and 80% in the independent samples. The diagnostic accuracy of SCNs was comparable with that of conventional volumetric MRI markers including whole brain volume and hippocampal volume. SCN did not significantly increase diagnostic accuracy beyond that of conventional MRI markers. We found the temporal SCN to be associated with verbal memory at baseline. No other associations with cognitive functions were seen. SCNs failed to predict the course of cognitive decline over an average of 18 months. We conclude that SCNs have diagnostic potential, but the diagnostic information gain beyond conventional MRI markers is limited.
- 5 May 2020
- Published by Frontiers Media SA in Frontiers in Psychiatry
- Vol. 11, 360
Open AccessGray Matter Covariance Networks as Classifiers and Predictors of Cognitive Function in Alzheimer’s Disease
The study of shared variation in gray matter morphology may define neurodegenerative diseases beyond what can be detected from the isolated assessment of regional brain volumes. We, therefore, aimed to (1) identify SCNs (structural covariance networks) that discriminate between Alzheimer’s disease (AD) patients and healthy controls (HC), (2) investigate their diagnostic accuracy in comparison and above established markers, and (3) determine if they are associated with cognitive abilities. We applied a random forest algorithm to identify discriminating networks from a set of 20 SCNs. The algorithm was trained on a main sample of 104 AD patients and 104 age-matched HC and was then validated in an independent sample of 28 AD patients and 28 controls from another center. Only two of the 20 SCNs contributed significantly to the discrimination between AD and controls. These were a temporal and a secondary somatosensory SCN. Their diagnostic accuracy was 74% in the original cohort and 80% in the independent samples. The diagnostic accuracy of SCNs was comparable with that of conventional volumetric MRI markers including whole brain volume and hippocampal volume. SCN did not significantly increase diagnostic accuracy beyond that of conventional MRI markers. We found the temporal SCN to be associated with verbal memory at baseline. No other associations with cognitive functions were seen. SCNs failed to predict the course of cognitive decline over an average of 18 months. We conclude that SCNs have diagnostic potential, but the diagnostic information gain beyond conventional MRI markers is limited.