Evidence map›Paper›PMID 26413208›Full record

ArticleProceedings. IEEE International Symposium on Biomedical Imaging2015

Information-Theoretic Characterization of Blood Panel Predictors for Brain Atrophy and Cognitive Decline in the Elderly.

Sarah K Madsen, Greg Ver Steeg, Adam Mezher, Neda Jahanshad, Talia M Nir, Xue Hua, Boris A Gutman, Aram Galstyan, Paul M Thompson, Alzheimer’s Disease Neuroimaging Initiative (ADNI)

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Proceedings. IEEE International Symposium on Biomedical Imaging, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Review
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.

Sarah K MadsenImaging Genetics Center, USC, Marina Del Rey, CA, USA.
Greg Ver SteegUSC Information Sciences Institute, Marina Del Rey, CA, USA.
Adam MezherImaging Genetics Center, USC, Marina Del Rey, CA, USA.
Neda JahanshadImaging Genetics Center, USC, Marina Del Rey, CA, USA.
Talia M NirImaging Genetics Center, USC, Marina Del Rey, CA, USA.
Xue HuaImaging Genetics Center, USC, Marina Del Rey, CA, USA.
Boris A GutmanImaging Genetics Center, USC, Marina Del Rey, CA, USA.
Aram GalstyanUSC Information Sciences Institute, Marina Del Rey, CA, USA.
Paul M ThompsonImaging Genetics Center, USC, Marina Del Rey, CA, USA.
Alzheimer’s Disease Neuroimaging Initiative (ADNI)

Funding

TR&D3: Intrinsic Surface MappingP41EB015922 · NIBIB · UNIVERSITY OF SOUTHERN CALIFORNIA · PI VAN HORN, JOHN DARRELL · 2012 to 2022
$14.1M
Training U54EB020403 · NIBIB · UNIVERSITY OF SOUTHERN CALIFORNIA · PI THOMPSON, PAUL M · 2014 to 2018
$10.1M
Growth factors, neuroinflammation, exercise, and brain integrityRF1AG041915 · NIA · UNIVERSITY OF SOUTHERN CALIFORNIA · PI THOMPSON, PAUL M · 2014 to 2014
$2.9M
Alzheimer's disease risk analyzed using population imaging genomicsR01AG040060 · NIA · UNIVERSITY OF SOUTHERN CALIFORNIA · PI THOMPSON, PAUL M · 2011 to 2016
$2.5M
Predicting Brain Changes in HIV/AIDSR01NS080655 · NINDS · UNIVERSITY OF SOUTHERN CALIFORNIA · PI NAVIA, BRADFORD, THOMPSON, PAUL M · 2012 to 2015
$2.3M
NIA NIH HHS R01 AG040060NIA NIH HHS RF1 AG041915NIBIB NIH HHS P41 EB015922NIBIB NIH HHS U54 EB020403NINDS NIH HHS R01 NS080655
6 · The paper itself

Abstract

Cognitive decline in old age is tightly linked with brain atrophy, causing significant burden. It is critical to identify which biomarkers are most predictive of cognitive decline and brain atrophy in the elderly. In 566 older adults from the Alzheimer's Disease Neuroimaging Initiative (ADNI), we used a novel unsupervised machine learning approach to evaluate an extensive list of more than 200 potential brain, blood and cerebrospinal fluid (CSF)-based predictors of cognitive decline. The method, called CorEx, discovers groups of variables with high multivariate mutual information and then constructs latent factors that explain these correlations. The approach produces a hierarchical structure and the predictive power of biological variables and latent factors are compared with regression. We found that a group of variables containing the well-known AD risk gene APOE and CSF tau and amyloid levels were highly correlated. This latent factor was the most predictive of cognitive decline and brain atrophy.

Indexed as

BrainCells & moleculesGenesMachine learningMagnetic resonance imaging (MRI)

Identifiers

PMID26413208
PMCPMC4578218

What OpenQuestion holds

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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.