Evidence map›Paper›PMID 37719837›Full record

ArticleImaging neuroscience (Cambridge, Mass.)2023

The temporal event-based model: Learning event timelines in progressive diseases.

Peter A Wijeratne, Arman Eshaghi, William J Scotton, Maitrei Kohli, Leon Aksman, Neil P Oxtoby, Dorian Pustina, John H Warner, Jane S Paulsen, Rachael I Scahill and 3 more

Abstract read
In one paragraph

Article in Imaging neuroscience (Cambridge, Mass.), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

The trial behind it

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

10 citing papers in PubMed.

  1. Article
  2. Article
  3. Stage-Aware Event-Based Modeling (SA-EBM) for Disease Progression.Proceedings of machine learning research · 2025
    Article
  4. Article
  5. Article
  6. Article
  7. Adaptive Subtype and Stage Inference for Alzheimer's Disease.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2024
    Article
  8. Article
  9. Review
  10. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors.

Peter A WijeratneUCL Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom.
Arman EshaghiQueen Square Multiple Sclerosis Centre, Department of Neuroinflammation, University College London, London, United Kingdom.
William J ScottonDementia Research Centre, Department of Neurodegenerative Disease, University College London, London, United Kingdom.
Maitrei KohliUCL Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom.
Leon AksmanKeck School of Medicine, University of Southern California, Los Angeles, California, United States.
Neil P OxtobyUCL Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom.
Dorian PustinaCHDI Management/CHDI Foundation, Princeton, New Jersey, United States.
John H WarnerCHDI Management/CHDI Foundation, Princeton, New Jersey, United States.
Jane S PaulsenDepartments of Neurology and Psychiatry, Carver College of Medicine, University of Iowa, Iowa City, Iowa, United States.
Rachael I ScahillHuntington's Disease Centre, Department of Neurodegenerative Disease, University College London, Queen Square, London, United Kingdom.
Cristina SampaioCHDI Management/CHDI Foundation, Princeton, New Jersey, United States.
Sarah J TabriziHuntington's Disease Centre, Department of Neurodegenerative Disease, University College London, Queen Square, London, United Kingdom.
Daniel C AlexanderUCL Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom.

Funding

NEUROBIOLOGICAL PREDICTORS OF HUNTINGTON'S DISEASER01NS040068 · NINDS · UNIVERSITY OF IOWA · PI PAULSEN, JANE S · 2001 to 2013
$48.5M
Preparing for preventive clinical trials in Huntington's diseaseU01NS105509 · NINDS · UNIVERSITY OF WISCONSIN-MADISON · PI PAULSEN, JANE S · 2020 to 2025
$2.5M
Integrated approach to protein biomarker identification in Huntington DiseaseU01NS082089 · NINDS · UNIVERSITY OF IOWA · PI CHELSKY, DANIEL, PAULSEN, JANE S · 2013 to 2015
$1.7M
Statistical disease modeling and clinimetrics to prepare for preventive trials in huntington diseaseU01NS103475 · NINDS · UNIVERSITY OF WISCONSIN-MADISON · PI PAULSEN, JANE S · 2020 to 2022
$1.7M
Statistical disease modeling and clinimetrics to prepare for preventive trials in huntington diseaseR01NS103475 · NINDS · UNIVERSITY OF IOWA · PI PAULSEN, JANE S, ZHANG, YING · 2017 to 2018
$1.2M
Preparing for preventive clinical trials in Huntington's diseaseR01NS105509 · NINDS · UNIVERSITY OF IOWA · PI PAULSEN, JANE S · 2018 to 2018
$658k
Medical Research Council MR/T027770/1NINDS NIH HHS R01 NS040068NINDS NIH HHS R01 NS103475NINDS NIH HHS R01 NS105509NINDS NIH HHS U01 NS082089NINDS NIH HHS U01 NS103475NINDS NIH HHS U01 NS105509Wellcome Trust
6 · The paper itself

Abstract

Timelines of events, such as symptom appearance or a change in biomarker value, provide powerful signatures that characterise progressive diseases. Understanding and predicting the timing of events is important for clinical trials targeting individuals early in the disease course when putative treatments are likely to have the strongest effect. However, previous models of disease progression cannot estimate the time between events and provide only an ordering in which they change. Here, we introduce the temporal event-based model (TEBM), a new probabilistic model for inferring timelines of biomarker events from sparse and irregularly sampled datasets. We demonstrate the power of the TEBM in two neurodegenerative conditions: Alzheimer's disease (AD) and Huntington's disease (HD). In both diseases, the TEBM not only recapitulates current understanding of event orderings but also provides unique new ranges of timescales between consecutive events. We reproduce and validate these findings using external datasets in both diseases. We also demonstrate that the TEBM improves over current models; provides unique stratification capabilities; and enriches simulated clinical trials to achieve a power of

Indexed as

Disease progression modelMarkov jump processneurodegenerationprognosistime series analysis

Identifiers

PMID37719837
PMCPMC10503481

What Socratic holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

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 Socratic graph.