Evidence map›Paper›PMID 31319126›Full record

ArticleMagnetic resonance imaging2019

Using deep Siamese neural networks for detection of brain asymmetries associated with Alzheimer's Disease and Mild Cognitive Impairment.

Chin-Fu Liu, Shreyas Padhy, Sandhya Ramachandran, Victor X Wang, Andrew Efimov, Alonso Bernal, Linyuan Shi, Marc Vaillant, J Tilak Ratnanather, Andreia V Faria and 5 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Magnetic resonance imaging, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed, 1 pooled it
–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

13 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. [Research on the application of convolution neural network in the diagnosis of Alzheimer's disease].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2021
    Pooled it
  2. Article
  3. Review
  4. Article
  5. Article
  6. Article
  7. Article
  8. Regularized regression on compositional trees with application to MRI analysis.Journal of the Royal Statistical Society. Series C, Applied statistics · 2022
    Article
  9. Article
  10. Review
  11. Article
  12. AI in MRI: A case for grassroots deep learning.Magnetic resonance imaging · 2019
    Article
  13. 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

15 authors.

Chin-Fu LiuCenter for Imaging Science, Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, USA; Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA. Electronic address: cliu104@jhu.edu.
Shreyas PadhyCenter for Imaging Science, Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, USA; Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA. Electronic address: shreyas@jhu.edu.
Sandhya RamachandranCenter for Imaging Science, Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, USA; Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA.
Victor X WangCenter for Imaging Science, Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, USA; Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA.
Andrew EfimovCenter for Imaging Science, Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, USA; Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA; Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, USA.
Alonso BernalCenter for Imaging Science, Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, USA; Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA.
Linyuan ShiCenter for Imaging Science, Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, USA; Department of Neuroscience, Johns Hopkins University, Baltimore, MD, USA.
Marc VaillantAnimetrics Inc., Conway, NH, USA.
J Tilak RatnanatherCenter for Imaging Science, Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, USA; Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA.
Andreia V FariaDepartment of Radiology, School of Medicine, Johns Hopkins University, Baltimore, MD.
Brian CaffoDepartment of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA; Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA; Kavli Neuroscience Discovery Institute, Johns Hopkins University, Baltimore, MD, USA.
Marilyn AlbertDepartment of Neurology, Johns Hopkins University, Baltimore, MD, USA.
Michael I MillerCenter for Imaging Science, Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, USA; Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA; Kavli Neuroscience Discovery Institute, Johns Hopkins University, Baltimore, MD, USA. Electronic address: mim@cis.jhu.edu.
BIOCARD Research Team
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
Project 2 MeasurementU19AG033655 · NIA · JOHNS HOPKINS UNIVERSITY · PI Abhay Moghekar · 2014 to 2026
$51.5M
Research Education ComponentP30AG066507 · NIA · JOHNS HOPKINS UNIVERSITY · PI Corinne Pettigrew · 2020 to 2026
$29.3M
TR&D 4: Algorithms for functional and anatomical brain analysisP41EB015909 · NIBIB · HUGO W. MOSER RES INST KENNEDY KRIEGER · PI VAN ZIJL, PETER CM · 2012 to 2020
$13.1M
Biomarkers of Cognitive Decline among Normal Individuals: The BIOCARD cohortU01AG033655 · NIA · JOHNS HOPKINS UNIVERSITY · PI ALBERT, MARILYN S. · 2009 to 2013
$11.2M
Neurodegenerative and Neurodevelopmental Subcortical Shape DiffeomorphometryR01EB020062 · NIBIB · JOHNS HOPKINS UNIVERSITY · PI MILLER, MICHAEL I, MOSTOFSKY, STEWART H · 2016 to 2019
$2.8M
NIA NIH HHS P30 AG066507NIA NIH HHS U01 AG024904NIA NIH HHS U01 AG033655NIA NIH HHS U19 AG033655NIBIB NIH HHS P41 EB015909NIBIB NIH HHS R01 EB020062
6 · The paper itself

Abstract

In recent studies, neuroanatomical volume and shape asymmetries have been seen during the course of Alzheimer's Disease (AD) and could potentially be used as preclinical imaging biomarkers for the prediction of Mild Cognitive Impairment (MCI) and AD dementia. In this study, a deep learning framework utilizing Siamese neural networks trained on paired lateral inter-hemispheric regions is used to harness the discriminative power of whole-brain volumetric asymmetry. The method uses the MRICloud pipeline to yield low-dimensional volumetric features of pre-defined atlas brain structures, and a novel non-linear kernel trick to normalize these features to reduce batch effects across datasets and populations. By working with the low-dimensional features, Siamese networks were shown to yield comparable performance to studies that utilize whole-brain MR images, with the advantage of reduced complexity and computational time, while preserving the biological information density. Experimental results also show that Siamese networks perform better in certain metrics by explicitly encoding the asymmetry in brain volumes, compared to traditional prediction methods that do not use the asymmetry, on the ADNI and BIOCARD datasets.

Indexed as

Neural Networks, ComputerAgedAged, 80 and overAlzheimer DiseaseBrainCognitive DysfunctionFemaleHumansMagnetic Resonance ImagingMaleAlzheimer's DiseaseDeep learningMachine learningMild Cognitive ImpairmentSiamese networksStructural magnetic resonance imaging

Identifiers

PMID31319126
PMCPMC6874905

What OpenQuestion holds

Textmetadata
LicenceTDM
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.