期刊
BRAIN CONNECTIVITY
卷 4, 期 5, 页码 384-393出版社
MARY ANN LIEBERT, INC
DOI: 10.1089/brain.2014.0235
关键词
Alzheimer's disease; amyloid plaques; brain connectomics; cortical thickness; directed networks
资金
- National Institute of Biomedical Imaging and Bioengineering (NIBIB) [T32 EB001631-05]
- National Institute of Neurological Disorders and Stroke [K25 NS-703689-01]
- Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health) [U01 AG024904]
- National Institute on Aging
- National Institute of Biomedical Imaging and Bioengineering
- Abbott
- Alzheimer's Association
- Alzheimer's Drug Discovery Foundation
- Amorfix Life Sciences Ltd.
- AstraZeneca
- Bayer HealthCare
- BioClinica, Inc.
- Eli Lilly and Company
- F. Hoffmann-La Roche Ltd
- Genentech, Inc.
- GE Healthcare
- Innogenetics, N. V.
- IXICO Ltd.
- Janssen Alzheimer Immunotherapy Research & Development, LLC.
- Johnson & Johnson Pharmaceutical Research & Development LLC.
- Medpace, Inc.
- Merck Co., Inc.
- Meso Scale Diagnostics, LLC.
- Novartis Pharmaceuticals Corporation
- Pfizer, Inc.
- Servier
- Synarc, Inc.
- Takeda Pharmaceutical Company
- Canadian Institutes of Health Research
- NIH [P30 AG010129, K01 AG030514]
This article introduces a new approach in brain connectomics aimed at characterizing the temporal spread in the brain of pathologies like Alzheimer's disease (AD). The main instrument is the development of directed progression networks (DPNets), wherein one constructs directed edges between nodes based on (weakly) inferred directions of the temporal spreading of the pathology. This stands in contrast to many previously studied brain networks where edges represent correlations, physical connections, or functional progressions. In addition, this is one of a few studies showing the value of using directed networks in the study of AD. This article focuses on the construction of DPNets for AD using longitudinal cortical thickness measurements from magnetic resonance imaging data. The network properties are then characterized, providing new insights into AD progression, as well as novel markers for differentiating normal cognition (NC) and AD at the group level. It also demonstrates the important role of nodal variations for network classification (i.e., the significance of standard deviations, not just mean values of nodal properties). Finally, the DPNets are utilized to classify subjects based on their global network measures using a variety of data-mining methodologies. In contrast to most brain networks, these DPNets do not show high clustering and small-world properties.
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