Evidence map›Paper›PMID 31372540›Full record

ArticleHeliyon2019

Extended multimodal whole-brain anatomical covariance analysis: detection of disrupted correlation networks related to amyloid deposition.

Chenfei Ye, Marilyn Albert, Timothy Brown, Murat Bilgel, Johnny Hsu, Ting Ma, Brian Caffo, Michael I Miller, Susumu Mori, Kenichi Oishi

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Heliyon, 2019. 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
0.7field-weighted citation impact, top 28% 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

2 citing papers in PubMed, 5 citations in OpenAlex.

  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

10 authors at 5 institutions in 2 countries.

Chenfei YeDepartment of Electronics and Information, Harbin Institute of Technology at Shenzhen, Shenzhen, Guangdong Province, China.
Marilyn AlbertDepartment of Neurology, The Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Timothy BrownCenter for Imaging Science, The Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Murat BilgelLaboratory of Behavioral Neuroscience, Intramural Research Program, National Institute on Aging, Baltimore, MD, USA.
Johnny HsuPeng Cheng Laboratory, Shenzhen, Guangdong, China.
Ting MaDepartment of Electronics and Information, Harbin Institute of Technology at Shenzhen, Shenzhen, Guangdong Province, China.
Brian CaffoDepartment of Biostatistics, Johns Hopkins University, Baltimore, MD, USA.
Michael I MillerCenter for Imaging Science, The Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Susumu MoriThe Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Kenichi OishiThe Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Johns Hopkins University · USKennedy Krieger Institute · USNational Institute on Aging · USPeng Cheng Laboratory · CNShenzhen Institute of Information Technology · CN

Funding

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
NIA NIH HHS P30 AG066507NIA NIH HHS U19 AG033655NIBIB NIH HHS P41 EB015909
6 · The paper itself

Abstract

backgroundAn anatomical covariance analysis (ACA) enables to elucidate inter-regional connections on a group basis, but little is known about the connections among white matter structures or among gray and white matter structures. Effect of including multiple magnetic resonance imaging (MRI) modalities into ACA framework in detecting white-to-white or gray-to-white connections is yet to be investigated. NEW

methodProposed extended anatomical covariance analysis (eACA), analyzes correlations among gray and white matter structures (multi-structural) in various types of imaging modalities (T1-weighted images, T2 maps obtained from dual-echo sequences, and diffusion tensor images (DTI)). To demonstrate the capability to detect a disruption of the correlation network affected by pathology, we applied the eACA to two groups of cognitively-normal elderly individuals, one with (PiB+) and one without (PiB-) amyloid deposition in their brains.

resultsThe volume of each anatomical structure was symmetric and functionally related structures formed a cluster. The pseudo-T2 value was highly homogeneous across the entire cortex in the PiB- group, while a number of physiological correlations were altered in the PiB + group. The DTI demonstrated unique correlation network among structures within the same phylogenetic portions of the brain that were altered in the PiB + group. COMPARISON WITH EXISTING

methodThe proposed eACA expands the concept of existing ACA to the connections among the white matter structures. The extension to other image modalities expands the way in which connectivity may be detected.

conclusionThe eACA has potential to evaluate alterations of the anatomical network related to pathological processes.

Indexed as

AmyloidAnatomical covarianceCorrelation networkDiffusion tensor imagingMagnetic resonance imagingNeurosciencePositron emission tomographyT2 relaxation

Identifiers

PMID31372540
PMCPMC6656959
OpenAlexW2964164810

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.