期刊
出版社
WILEY
DOI: 10.1002/trc2.12303
关键词
Alzheimer's disease; machine learning; multi-scale brain simulation; positron emission tomography; The Virtual Brain
资金
- Alzheimer's Disease Neuroimaging Initiative (ADNI
- National Institutes of Health) [U01 AG024904]
- DOD ADNI (Department of Defense) [W81XWH-12-2-0012]
- National Institute on Aging
- National Institute of Biomedical Imaging and Bioengineering
- AbbVie
- Alzheimer's Association
- Alzheimer's Drug Discovery Foundation
- Araclon Biotech
- BioClinica, Inc.
- Biogen
- Bristol-Myers SquibbCompany
- CereSpir, Inc.
- Cogstate
- Eisai Inc.
- Elan Pharmaceuticals, Inc.
- Eli Lilly and Company
- EuroImmun
- F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.
- Fujirebio
- GE Healthcare
- IXICO Ltd.
- Janssen Alzheimer Immunotherapy Research & Development, LLC
- Johnson & Johnson Pharmaceutical Research & Development LLC
- Lumosity
- Lundbeck
- Merck Co., Inc.
- Meso Scale Diagnostics, LLC
- NeuroRx Research
- Neurotrack Technologies
- Novartis Pharmaceuticals Corporation
- Pfizer Inc.
- Piramal Imaging
- Servier
- Takeda Pharmaceutical Company
- Transition Therapeutics
- Canadian Institutes of Health Research
- EU [826421, 785907]
- ERC [945539, 683049]
- German Research Foundation [SFB 1436, 425899996, SFB 1315, 327654276, SFB 936, 178316478, SFB-TRR 295, 424778381, RI 2073/6-1, RI 2073/10-2, RI 2073/9-1]
- PHRASE Horizon EIC grant [101058240]
- Berlin Institute of Health & Foundation Charite, Johanna Quandt Excellence Initiative
- ERAPerMed PatternCog
- European Research Council (ERC) [683049] Funding Source: European Research Council (ERC)
Computational brain network modeling using The Virtual Brain (TVB) simulation platform, combined with machine learning and multi-modal neuroimaging, can reveal mechanisms and improve diagnostics in Alzheimer's disease (AD). This study enhances whole-brain simulation by linking local amyloid beta (Aβ) positron emission tomography (PET) with altered excitability, and demonstrates improved classification accuracy when combining empirical neuroimaging features and simulated local field potentials (LFPs).
Introduction Computational brain network modeling using The Virtual Brain (TVB) simulation platform acts synergistically with machine learning (ML) and multi-modal neuroimaging to reveal mechanisms and improve diagnostics in Alzheimer's disease (AD). Methods We enhance large-scale whole-brain simulation in TVB with a cause-and-effect model linking local amyloid beta (A beta) positron emission tomography (PET) with altered excitability. We use PET and magnetic resonance imaging (MRI) data from 33 participants of the Alzheimer's Disease Neuroimaging Initiative (ADNI3) combined with frequency compositions of TVB-simulated local field potentials (LFP) for ML classification. Results The combination of empirical neuroimaging features and simulated LFPs significantly outperformed the classification accuracy of empirical data alone by about 10% (weighted F1-score empirical 64.34% vs. combined 74.28%). Informative features showed high biological plausibility regarding the AD-typical spatial distribution. Discussion The cause-and-effect implementation of local hyperexcitation caused by A beta can improve the ML-driven classification of AD and demonstrates TVB's ability to decode information in empirical data using connectivity-based brain simulation.
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