ArticleBrain : a journal of neurology2018
A method for inferring regional origins of neurodegeneration.
Article in Brain : a journal of neurology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 32 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
32 citing papers in PubMed, 1 synthesis or guideline pooled it, 44 citations in OpenAlex.
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- Data-driven modelling of neurodegenerative disease progression: thinking outside the black box.Nature reviews. Neuroscience · 2024Review
- On propagation in networks, promising models beyond network diffusion to describe degenerative brain diseases and traumatic brain injuries.Frontiers in pharmacology · 2024Article
- Connectome-based biophysics models of Alzheimer's disease diagnosis and prognosis.Translational research : the journal of laboratory and clinical medicine · 2023Review
- lncRNA-associated ceRNA network revealing the potential regulatory roles of ferroptosis and immune infiltration in Alzheimer's disease.Frontiers in aging neuroscience · 2023Article
- The effects of microglia on tauopathy progression can be quantified using Nexopathy in silico (Nexis) models.Scientific reports · 2022Article
- Network diffusion model predicts neurodegeneration in limb-onset Amyotrophic Lateral Sclerosis.PloS one · 2022Article
- Graph Models of Pathology Spread in Alzheimer's Disease: An Alternative to Conventional Graph Theoretic Analysis.Brain connectivity · 2021Review
- Combined Model of Aggregation and Network Diffusion Recapitulates Alzheimer's Regional Tau-Positron Emission Tomography.Brain connectivity · 2021Article
- The human connectome in Alzheimer disease - relationship to biomarkers and genetics.Nature reviews. Neurology · 2021Review
- Longitudinal predictive modeling of tau progression along the structural connectome.NeuroImage · 2021Article
- Bayesian Physics-Based Modeling of Tau Propagation in Alzheimer's Disease.Frontiers in physiology · 2021Article
- Network-constrained technique to characterize pathology progression rate in Alzheimer's disease.Brain communications · 2021Article
- A physics-informed geometric learning model for pathological tau spread in Alzheimer's disease.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2020Article
- Spread of pathological tau proteins through communicating neurons in human Alzheimer's disease.Nature communications · 2020Article
Corrections and comments
- Erratum issuedCorrigendum.2018
Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
Alzheimer's disease, the most common form of dementia, is characterized by the emergence and spread of senile plaques and neurofibrillary tangles, causing widespread neurodegeneration. Though the progression of Alzheimer's disease is considered to be stereotyped, the significant variability within clinical populations obscures this interpretation on the individual level. Of particular clinical importance is understanding where exactly pathology, e.g. tau, emerges in each patient and how the incipient atrophy pattern relates to future spread of disease. Here we demonstrate a newly developed graph theoretical method of inferring prior disease states in patients with Alzheimer's disease and mild cognitive impairment using an established network diffusion model and an L1-penalized optimization algorithm. Although the 'seeds' of origin using our inference method successfully reproduce known trends in Alzheimer's disease staging on a population level, we observed that the high degree of heterogeneity between patients at baseline is also reflected in their seeds. Additionally, the individualized seeds are significantly more predictive of future atrophy than a single seed placed at the hippocampus. Our findings illustrate that understanding where disease originates in individuals is critical to determining how it progresses and that our method allows us to infer early stages of disease from atrophy patterns observed at diagnosis.
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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.