Evidence map›Paper›PMID 34899166›Full record

ArticleFrontiers in neuroscience2021

Federated Morphometry Feature Selection for Hippocampal Morphometry Associated Beta-Amyloid and Tau Pathology.

Jianfeng Wu, Qunxi Dong, Jie Zhang, Yi Su, Teresa Wu, Richard J Caselli, Eric M Reiman, Jieping Ye, Natasha Lepore, Kewei Chen and 2 more

Open access · goldAbstract read
In one paragraph

Article in Frontiers in neuroscience, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
0.7field-weighted citation impact, top 30% 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

7 citing papers in PubMed, 11 citations in OpenAlex.

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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

12 authors at 6 institutions in 2 countries.

Jianfeng WuSchool of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, United States.
Qunxi DongSchool of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, United States.
Jie ZhangSchool of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, United States.
Yi SuBanner Alzheimer's Institute, Phoenix, AZ, United States.
Teresa WuSchool of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, United States.
Richard J CaselliDepartment of Neurology, Mayo Clinic Arizona, Scottsdale, AZ, United States.
Eric M ReimanBanner Alzheimer's Institute, Phoenix, AZ, United States.
Jieping YeDepartment of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, United States.
Natasha LeporeCIBORG Lab, Department of Radiology, Children's Hospital Los Angeles, Los Angeles, CA, United States.
Kewei ChenBanner Alzheimer's Institute, Phoenix, AZ, United States.
Paul M ThompsonImaging Genetics Center, Stevens Neuroimaging and Informatics Institute, University of Southern California, Marina del Rey, CA, United States.
Yalin WangSchool of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, United States.
Arizona State University · USBanner Alzheimer’s InstituteChildren's Hospital of Los Angeles · USMayo Clinic in Arizona · USUniversity of Michigan–Ann Arbor · USUniversity of Southern California · US

Funding

Research Education ComponentP30AG019610 · NIA · SUN HEALTH RESEARCH INSTITUTE · PI REIMAN, ERIC MICHAEL · 2001 to 2020
$32.5M
Ultrascale Machine Learning to Empower Discovery in Alzheimers Disease BiobanksU01AG068057 · NIA · UNIVERSITY OF SOUTHERN CALIFORNIA · PI Christos Davatzikos, Heng Huang · 2020 to 2026
$20.7M
PET, APOE & the Preclinical Course of Alzheimer's DiseaseR01AG031581 · NIA · BANNER ALZHEIMER'S INSTITUTE · PI CASELLI, RICHARD J., REIMAN, ERIC MICHAEL · 2008 to 2018
$16.6M
Early joint cranial and brain development from fetal and pediatric imagingR01DE030286 · NIDCR · CHILDREN'S HOSPITAL OF LOS ANGELES · PI LEPORE, NATASHA, LINGURARU, MARIUS GEORGE · 2021 to 2025
$3.5M
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
Predicting the early childhood outcomes of preterm brain shape abnormalitiesR01EB025032 · NIBIB · CHILDREN'S HOSPITAL OF LOS ANGELES · PI LEPORE, NATASHA · 2017 to 2020
$2.6M
Hierarchical Bayesian Analysis of Retinotopic Maps of the Human Visual Cortex with Conformal GeometryR01EY032125 · NEI · ARIZONA STATE UNIVERSITY-TEMPE CAMPUS · PI LU, ZHONG-LIN, WANG, YALIN · 2021 to 2024
$1.2M
Developing a Univariate Neurodegeneration Imaging Biomarker with Optimal TransportationR21AG065942 · NIA · ARIZONA STATE UNIVERSITY-TEMPE CAMPUS · PI WANG, YALIN · 2020 to 2020
$445k
NEI NIH HHS R01 EY032125NIA NIH HHS P30 AG019610NIA NIH HHS R01 AG031581NIA NIH HHS R21 AG065942NIA NIH HHS RF1 AG051710NIA NIH HHS U01 AG068057NIBIB NIH HHS R01 EB025032NIDCR NIH HHS R01 DE030286
6 · The paper itself

Abstract

Amyloid-β (Aβ) plaques and tau protein tangles in the brain are now widely recognized as the defining hallmarks of Alzheimer's disease (AD), followed by structural atrophy detectable on brain magnetic resonance imaging (MRI) scans. One of the particular neurodegenerative regions is the hippocampus to which the influence of Aβ/tau on has been one of the research focuses in the AD pathophysiological progress. This work proposes a novel framework, Federated Morphometry Feature Selection (FMFS) model, to examine subtle aspects of hippocampal morphometry that are associated with Aβ/tau burden in the brain, measured using positron emission tomography (PET). FMFS is comprised of hippocampal surface-based feature calculation, patch-based feature selection, federated group LASSO regression, federated screening rule-based stability selection, and region of interest (ROI) identification. FMFS was tested on two Alzheimer's Disease Neuroimaging Initiative (ADNI) cohorts to understand hippocampal alterations that relate to Aβ/tau depositions. Each cohort included pairs of MRI and PET for AD, mild cognitive impairment (MCI), and cognitively unimpaired (CU) subjects. Experimental results demonstrated that FMFS achieves an 89× speedup compared to other published state-of-the-art methods under five independent hypothetical institutions. In addition, the subiculum and

Indexed as

Alzheimer’s diseaseamyloid-β (Aβ)/taufederated learninghippocampal morphometrymagnetic resonance imaging (MRI)

Identifiers

PMID34899166
PMCPMC8655732
OpenAlexW3216362361

What OpenQuestion holds

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LicenceCC BY
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.