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Updated: Mar 30, 2026
Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
Published on: June 26, 2013
Based on the Network Degeneration Hypothesis: Separating Individual Patients with Different Neurodegenerative
Masoud Tahmasian1, Junming Shao2, Chun Meng3
1Department of Neuroradiology, Technische Universität München, Munich, Germany Department of Nuclear Medicine, Technische Universität München, Munich, Germany TUM-Neuroimaging Center (TUM-NIC), Technische Universität München, Munich, Germany Sleep Disorders Research Center, Kermanshah University of Medical Science (KUMS), Kermanshah, Iran Department of Neurology, University Hospital Cologne, Cologne, Germany Department of Nuclear Medicine, University Hospital Cologne, Cologne, Germany masoud.tahmasian@uk-koeln.de.
Unlabelled:
The network degeneration hypothesis (NDH) of neurodegenerative syndromes suggests that pathologic brain changes distribute primarily along distinct brain networks, which are characteristic for different syndromes. Brain changes of neurodegenerative syndromes can be characterized in vivo by different imaging modalities. Our aim was to test the hypothesis whether multimodal imaging based on the NDH separates individual patients with different neurodegenerative syndromes.
Methods:
Twenty patients with Alzheimer disease (AD) and 20 patients with frontotemporal lobar degeneration (behavioral variant frontotemporal dementia [bvFTD, n = 11], semantic dementia [SD, n = 4], or progressive nonfluent aphasia [PNFA, n = 5]) underwent simultaneous MRI and (18)F-FDG PET in a hybrid PET/MR scanner. The 3 outcome measures were voxelwise values of degree centrality as a surrogate for regional functional connectivity, glucose metabolism as a surrogate for regional metabolism, and volumetric-based morphometry as a surrogate for regional gray matter volume. Outcome measures were derived from predefined core regions of 4 intrinsic networks based on the NDH, which have been demonstrated to be characteristic for AD, bvFTD, SD, and PNFA, respectively. Subsequently, we applied support vector machine to classify individual patients via combined imaging measures, and results were evaluated by leave-one-out cross-validation.
Results:
On the basis of multimodal voxelwise regional patterns, classification accuracies for separating patients with different neurodegenerative syndromes were 77.5% for AD versus others, 82.5% for bvFTD versus others, 97.5% for SD versus others, and 87.5% for PNFA versus others. Multimodal classification results were significantly superior to unimodal approaches.
Conclusion:
Our finding provides initial evidence that the combination of regional metabolism, functional connectivity, and gray matter volume, which were derived from disease characteristic networks, separates individual patients with different neurodegenerative syndromes. Preliminary results suggest that employing multimodal imaging guided by the NDH may generate promising biomarkers of neurodegenerative syndromes.
