ArticleAlzheimer's & dementia (New York, N. Y.)2022
Brain simulation augments machine-learning-based classification of dementia.
Article in Alzheimer's & dementia (New York, N. Y.), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.
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Who cites it
19 citing papers in PubMed, 1 synthesis or guideline pooled it, 35 citations in OpenAlex.
- Tracking electrophysiological signatures of Alzheimer's disease: a systematic review of multimodal studies.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025Pooled it
- Alterations in topological and dynamical parameters correlate with disease biomarkers and neuropsychological scores in prodromic stages of dementia.Scientific reports · 2026Article
- A digital twin approach for simultaneous reconstruction of brain anatomy and dynamics from neural data.PLOS digital health · 2026Article
- An Interactive Brain Atlas of Knowledge.bioRxiv : the preprint server for biology · 2026Article
- Excitation-inhibition homeostasis in Alzheimer's disease: a selective multiscale review of mechanisms, sex differences, and therapeutic opportunities.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2026Review
- Digital twins support cross-modal and cross-centric classification of mild cognitive impairment.Communications medicine · 2026Article
- Article
- Region-specific mean field models enhance simulations of local and global brain dynamics.NPJ systems biology and applications · 2025Article
- Digital twins and non-invasive recordings enable early diagnosis of Alzheimer's disease.Alzheimer's research & therapy · 2025Observational
- Altered intrinsic ignition dynamics linked to Amyloid-bioRxiv : the preprint server for biology · 2025Article
- Virtual brain twins: from basic neuroscience to clinical use.National science review · 2024Review
- Impaired long-range excitatory time scale predicts abnormal neural oscillations and cognitive deficits in Alzheimer's disease.Alzheimer's research & therapy · 2024Article
- Whole-brain modeling of the differential influences of amyloid-beta and tau in Alzheimer's disease.Alzheimer's research & therapy · 2023Article
- Impaired long-range excitatory time scale predicts abnormal neural oscillations and cognitive deficits in Alzheimer's disease.Research square · 2023Article
- Alzheimer's Disease: Insights from Large-Scale Brain Dynamics Models.Brain sciences · 2023Review
- Virtual brain simulations reveal network-specific parameters in neurodegenerative dementias.Frontiers in aging neuroscience · 2023Article
- Brain simulation augments machine-learning-based classification of dementia.Alzheimer's & dementia (New York, N. Y.) · 2022Article
- The Virtual Brain links transcranial magnetic stimulation evoked potentials and inhibitory neurotransmitter changes in major depressive disorder.Imaging neuroscience (Cambridge, Mass.)Article
- Personalized modeling of Alzheimer's disease progression estimates neurodegeneration severity from EEG recordings.Alzheimer's & dementia (Amsterdam, Netherlands)Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors at 7 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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 (Aβ) 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 Aβ 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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.