Evidence map›Paper›PMID 35321154›Full record

ArticleProceedings. IEEE International Symposium on Biomedical Imaging2021

Predicting Progression from Mild Cognitive Impairment to Alzheimer's Disease using MRI-based Cortical Features and a Two-State Markov Model.

Eleonora Ficiarà, Valentino Crespi, Shruti Prashant Gadewar, Sophia I Thomopoulos, Joshua Boyd, Paul M Thompson, Neda Jahanshad, Fabrizio Pizzagalli, Alzheimer’s Disease Neuroimaging Initiative

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Proceedings. IEEE International Symposium on Biomedical Imaging, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

9 authors.

Eleonora FiciaràDepartment of Neurosciences, University of Turin, Italy.
Valentino CrespiInformation Sciences Institute (ISI), AI Division, University of Southern California, USA.
Shruti Prashant GadewarImaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, Marina del Rey, CA, USA.
Sophia I ThomopoulosImaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, Marina del Rey, CA, USA.
Joshua BoydImaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, Marina del Rey, CA, USA.
Paul M ThompsonImaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, Marina del Rey, CA, USA.
Neda JahanshadImaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, Marina del Rey, CA, USA.
Fabrizio PizzagalliDepartment of Neurosciences, University of Turin, Italy.
Alzheimer’s Disease Neuroimaging Initiative

Funding

TR&D3: Intrinsic Surface MappingP41EB015922 · NIBIB · UNIVERSITY OF SOUTHERN CALIFORNIA · PI VAN HORN, JOHN DARRELL · 2012 to 2022
$14.1M
High resolution mapping of the genetic risk for disease in the aging brainR01AG059874 · NIA · UNIVERSITY OF SOUTHERN CALIFORNIA · PI JAHANSHAD, NEDA · 2018 to 2022
$3.1M
Multi-Source Sparse Learning to Identify MCI and Predict DeclineRF1AG051710 · NIA · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI THOMPSON, PAUL M, WANG, YALIN · 2016 to 2016
$2.8M
NIA NIH HHS R01 AG059874NIA NIH HHS RF1 AG051710NIBIB NIH HHS P41 EB015922
6 · The paper itself

Abstract

Magnetic resonance imaging (MRI) has a potential for early diagnosis of individuals at risk for developing Alzheimer's disease (AD). Cognitive performance in healthy elderly people and in those with mild cognitive impairment (MCI) has been associated with measures of cortical gyrification [1] and thickness (CT) [2], yet the extent to which sulcal measures can help to predict AD conversion above and beyond CT measures is not known. Here, we analyzed 721 participants with MCI from phases 1 and 2 of the Alzheimer's Disease Neuroimaging Initiative, applying a two-state Markov model to study the conversion from MCI to AD condition. Our preliminary results suggest that MRI-based cortical features, including sulcal morphometry, may help to predict conversion from MCI to AD.

Indexed as

Alzheimer’s diseaseMarkov modelMRIsulcal morphometry

Identifiers

PMID35321154
PMCPMC8935949

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Registered trials

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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.