Evidence map›Paper›PMID 37398392›Full record

ArticlemedRxiv : the preprint server for health sciences2023

Personalising Alzheimer's Disease progression using brain atrophy markers.

Serena Verdi, Saige Rutherford, Charlotte Fraza, Duygu Tosun, Andre Altmann, Lars Lau Raket, Jonathan M Schott, Andre F Marquand, James H Cole, Alzheimer’s Disease Neuroimaging Initiative

Abstract readPreprint
PubMed Full text at PMC Publisher
In one paragraph

Article in medRxiv : the preprint server for health sciences, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

10 authors.

Serena VerdiCentre for Medical Image Computing, University College London, London, UK.
Saige RutherfordDonders Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, 6525EN, the Netherlands.
Charlotte FrazaDonders Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, 6525EN, the Netherlands.
Duygu TosunDepartment of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA.
Andre AltmannCentre for Medical Image Computing, University College London, London, UK.
Lars Lau RaketDepartment of Clinical Sciences, Lund University, Lund, Sweden.
Jonathan M SchottDementia Research Centre, UCL Queen Square Institute of Neurology, London, UK.
Andre F MarquandDonders Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, 6525EN, the Netherlands.
James H ColeCentre for Medical Image Computing, University College London, London, UK.
Alzheimer’s Disease Neuroimaging Initiative

Funding

Alzheimer's Disease Neuroimaging Initiative - SupplementU01AG024904 · NIA · NORTHERN CALIFORNIA INSTITUTE RES &EDUC · PI WEINER, MICHAEL W · 2004 to 2015
$121.0M
NIA NIH HHS U01 AG024904
6 · The paper itself

Abstract

introductionNeuroanatomical normative modelling can capture individual variability in Alzheimer's Disease (AD). We used neuroanatomical normative modelling to track individuals' disease progression in people with mild cognitive impairment (MCI) and patients with AD.

methodsCortical thickness and subcortical volume neuroanatomical normative models were generated using healthy controls (n~58k). These models were used to calculate regional Z-scores in 4361 T1-weighted MRI time-series scans. Regions with Z-scores <-1.96 were classified as outliers and mapped on the brain, and also summarised by total outlier count (tOC).

resultsRate of change in tOC increased in AD and in people with MCI who converted to AD and correlated with multiple non-imaging markers. Moreover, a higher annual rate of change in tOC increased the risk of MCI progression to AD. Brain Z-score maps showed that the hippocampus had the highest rate of atrophy change.

conclusionsIndividual-level atrophy rates can be tracked by using regional outlier maps and tOC.

Identifiers

PMID37398392
PMCPMC10312850

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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.