Evidence map›Paper›PMID 16986115›Full record

ArticleNMR in biomedicine2006

Evaluation of treatment effects in Alzheimer's and other neurodegenerative diseases by MRI and MRS.

S G Mueller, N Schuff, M W Weiner

Open access · bronzeAbstract read
In one paragraph

Article in NMR in biomedicine, 2006. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
33citing papers in PubMed, 1 pooled it
5.7field-weighted citation impact, top 4% of its field
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

33 citing papers in PubMed, 1 synthesis or guideline pooled it, 63 citations in OpenAlex.

  1. Pooled it
  2. Review
  3. Review
  4. Article
  5. Review
  6. Article
  7. Review of Progress in Diagnostic Studies of Autism Spectrum Disorder Using Neuroimaging.Interdisciplinary sciences, computational life sciences · 2023
    Review
  8. Review
  9. Article
  10. Article
  11. Personalized Healthcare for Dementia.Healthcare (Basel, Switzerland) · 2021
    Review
  12. Article
  13. MR Imaging of SCA3/MJD.Frontiers in neuroscience · 2020
    Review
  14. Article
  15. Article
  16. Review
  17. Review
  18. Article
  19. Review
  20. Cerebral blood flow is an earlier indicator of perfusion abnormalities than cerebral blood volume in Alzheimer's disease.Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism · 2014
    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

3 authors at 2 institutions in 1 country.

S G MuellerCenter of Imaging of Neurodegenerative Diseases, Veterans Administration Medical Center, Department of Radiology, University of California San Francisco, San Francisco, CA 94121, USA.
N Schuff
M W Weiner
San Francisco VA Medical Center · USUniversity of California, San Francisco · US

Funding

TDP-43 Loss-of-Function: Biology to BiomarkersP01AG019724 · NIA · UNIVERSITY OF PENNSYLVANIA · PI BRUCE L MILLER · 2002 to 2026
$67.2M
Prediction of cognitive decline with structural, diffusion, and perfusion MRIR01AG010897 · NIA · UNIVERSITY OF CALIFORNIA SAN FRANCISCO · PI WEINER, MICHAEL W · 1992 to 2013
$9.4M
PHARMACOKINETICS AND PHARMACODYNAMICSP01AA011493 · NIAAA · NORTHERN CALIFORNIA INSTITUTE RES &EDUC · PI FEIN, GEORGE · 1998 to 2002
$6.0M
NIAAA NIH HHS P01 AA011493NIAAA NIH HHS P01 AA11493NIA NIH HHS P01 AG019724NIA NIH HHS P01 AG19724NIA NIH HHS R01 AG010897
6 · The paper itself

Abstract

Neurodegeneration refers to a large clinically and pathologically heterogeneous disease entity associated with slowly progressive neuronal loss in different anatomical and functional systems of the brain. Neurodegenerative diseases often affect cognition, e.g. Alzheimer's disease (AD), dementia with Lewy bodies and vascular dementia, or different aspects of the motor system, e.g., amyotrophic lateral sclerosis, Parkinson's disease and ataxic disorders. Owing to increasing knowledge about the mechanisms leading to neurodegeneration, the development of treatments able to modify the neurodegenerative process becomes possible for the first time. Currently, clinical outcome measures are used to assess the efficacy of such treatments. However, most clinical outcome measures have a low test-retest reliability and thus considerable measurement variance. Therefore, large patient populations and long observation times are needed to detect treatment effects. Furthermore, clinical outcome measures cannot distinguish between symptomatic and disease-modifying treatment effects. Therefore, alternative biomarkers including neuroimaging may take on a more important role in this process. Because MR scanners are widely available and allow for non-invasive detection and quantification of changes in brain structure and metabolism, there is increasing interest in the use of MRI/MRS to monitor objectively treatment effects in clinical trials of neurodegenerative diseases. Particularly volumetric MRI has been used to measure atrophy rates in treatment trials of AD because the relationship between atrophic changes and neuron loss is well established and correlates well with clinical measures. More research is needed to determine the value of other MR modalities, i.e. diffusion, perfusion and functional MRI and MR spectroscopy, for clinical trials with neuroprotective drugs.

Indexed as

Alzheimer DiseaseAmyotrophic Lateral SclerosisClinical Trials as TopicDementia, VascularHumansMagnetic Resonance ImagingMagnetic Resonance SpectroscopyNeurodegenerative DiseasesNeuroprotective AgentsParkinson DiseasePositron-Emission TomographyTomography, Emission-Computed, Single-PhotonTreatment OutcomeNeuroprotective Agents

Identifiers

PMID16986115
PMCPMC1820857
OpenAlexW2017009791

What OpenQuestion holds

Textmetadata
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 OpenQuestion graph.