Medizinische Universität Graz Austria/Österreich - Forschungsportal - Medical University of Graz
Gewählte Publikation:
SHR Neuro Krebs Kardio Lipid Stoffw Microb
Schouten, TM; Koini, M; de Vos, F; Seiler, S; van der Grond, J; Lechner, A; Hafkemeijer, A; Möller, C; Schmidt, R; de Rooij, M; Rombouts, SA.
Combining anatomical, diffusion, and resting state functional magnetic resonance imaging for individual classification of mild and moderate Alzheimer's disease.
Neuroimage Clin. 2016; 11:46-51
Doi: 10.1016/j.nicl.2016.01.002
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PubMed
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- Co-Autor*innen der Med Uni Graz
- Koini Marisa
- Lechner Anita
- Schmidt Reinhold
- Seiler Stephan
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- Abstract:
- 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. 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.
- Find related publications in this database (using NLM MeSH Indexing)
- Aged -
- Aged, 80 and over -
- Alzheimer Disease - diagnosis
- Alzheimer Disease - pathology
- Brain - pathology
- Brain - physiopathology
- Cognitive Dysfunction - classification
- Cognitive Dysfunction - diagnosis
- Cognitive Dysfunction - pathology
- Diffusion Tensor Imaging - methods
- Female -
- Gray Matter - pathology
- Humans -
- Magnetic Resonance Imaging - methods
- Male -
- Prospective Studies -
- White Matter - pathology
- Find related publications in this database (Keywords)
- Alzheimer's disease
- Classification
- Multimodal
- MRI
- fMRI
- DWI