Evidence map›Paper›PMID 33928076›Full record

ArticleFrontiers in physics2020

Optimizing Diffusion Imaging Protocols for Structural Connectomics in Mouse Models of Neurological Conditions.

Robert J Anderson, Christopher M Long, Evan D Calabrese, Scott H Robertson, G Allan Johnson, Gary P Cofer, Richard J O'Brien, Alexandra Badea

Open access · goldAbstract read
In one paragraph

Article in Frontiers in physics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed, 2 pooled it
1.6field-weighted citation impact, top 18% 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

15 citing papers in PubMed, 2 syntheses or guidelines pooled it, 27 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

8 authors at 2 institutions in 1 country.

Robert J AndersonDepartment of Radiology, Duke University, Durham, CA, United States.
Christopher M LongDepartment of Radiology, Duke University, Durham, CA, United States.
Evan D CalabreseDepartment of Radiology and Biomedical Imaging, University of California, San Francisco, CA, United States.
Scott H RobertsonDepartment of Radiology, Duke University, Durham, CA, United States.
G Allan JohnsonDepartment of Radiology, Duke University, Durham, CA, United States.
Gary P CoferDepartment of Radiology, Duke University, Durham, CA, United States.
Richard J O'BrienDepartment of Neurology, School of Medicine, Duke University, Durham, CA, United States.
Alexandra BadeaDepartment of Radiology, Duke University, Durham, CA, United States.
Duke University · USUniversity of California, San Francisco · US

Funding

INTEGRATED CENTER FOR IN VIVO MICROSCOPYP41EB015897 · NIBIB · DUKE UNIVERSITY · PI JOHNSON, GEORGE ALLAN · 2012 to 2017
$7.4M
Sex and APOE genotype interact to alter immune regulated metabolism in ADRF1AG057895 · NIA · DUKE UNIVERSITY · PI BADEA, ALEXANDRA, COLTON, CAROL ANNE · 2019 to 2019
$5.8M
Brain networks in mouse models of agingR01AG066184 · NIA · DUKE UNIVERSITY · PI BADEA, ALEXANDRA · 2019 to 2023
$3.8M
The Duke Preclinical Research Resources for Quantitative Imaging BiomarkersU24CA220245 · NCI · DUKE UNIVERSITY · PI BADEA, CRISTIAN T · 2017 to 2021
$2.8M
Immune-based nutrient deprivation and neurodegenerative diseaseR01AG045422 · NIA · DUKE UNIVERSITY · PI COLTON, CAROL ANNE · 2013 to 2017
$2.3M
Multiorgan Photon Counting CT and Machine Learning to Elucidate Aging Mechanisms and InterventionsR01AG070149 · NIA · DUKE UNIVERSITY · PI CRISTIAN T BADEA, Alexandra Badea · 2024 to 2026
$2.1M
Sex and APOE genotype interact to alter immune regulated metabolism in ADR56AG057895 · NIA · DUKE UNIVERSITY · PI BADEA, ALEXANDRA, COLTON, CAROL ANNE · 2017 to 2017
$1.1M
Quantitative MR Microscopy of Phenotypic Biomarkers in Alzheimer's DiseaseK01AG041211 · NIA · DUKE UNIVERSITY · PI BADEA, ALEXANDRA · 2014 to 2018
$642k
Immune regulated amino acid pathways in Alzheimer's DiseaseR56AG051765 · NIA · DUKE UNIVERSITY · PI COLTON, CAROL ANNE · 2016 to 2016
$623k
NCI NIH HHS U24 CA220245NIA NIH HHS K01 AG041211NIA NIH HHS R01 AG045422NIA NIH HHS R01 AG066184NIA NIH HHS R01 AG070149NIA NIH HHS R56 AG051765NIA NIH HHS R56 AG057895NIA NIH HHS RF1 AG057895NIBIB NIH HHS P41 EB015897
6 · The paper itself

Abstract

Network approaches provide sensitive biomarkers for neurological conditions, such as Alzheimer's disease (AD). Mouse models can help advance our understanding of underlying pathologies, by dissecting vulnerable circuits. While the mouse brain contains less white matter compared to the human brain, axonal diameters compare relatively well (e.g., ~0.6 μm in the mouse and ~0.65-1.05 μm in the human corpus callosum). This makes the mouse an attractive test bed for novel diffusion models and imaging protocols. Remaining questions on the accuracy and uncertainty of connectomes have prompted us to evaluate diffusion imaging protocols with various spatial and angular resolutions. We have derived structural connectomes by extracting gradient subsets from a high-spatial, high-angular resolution diffusion acquisition (120 directions, 43-μm-size voxels). We have simulated protocols with 12, 15, 20, 30, 45, 60, 80, 100, and 120 angles and at 43, 86, or 172-μm voxel sizes. The rotational stability of these schemes increased with angular resolution. The minimum condition number was achieved for 120 directions, followed by 60 and 45 directions. The percentage of voxels containing one dyad was exceeded by those with two dyads after 45 directions, and for the highest spatial resolution protocols. For the 86- or 172-μm resolutions, these ratios converged toward 55% for one and 39% for two dyads, respectively, with <7% from voxels with three dyads. Tractography errors, estimated through dyad dispersion, decreased most with angular resolution. Spatial resolution effects became noticeable at 172 μm. Smaller tracts, e.g., the fornix, were affected more than larger ones, e.g., the fimbria. We observed an inflection point for 45 directions, and an asymptotic behavior after 60 directions, corresponding to similar projection density maps. Spatially downsampling to 86 μm, while maintaining the angular resolution, achieved a subgraph similarity of 96% relative to the reference. Using 60 directions with 86- or 172-μm voxels resulted in 94% similarity. Node similarity metrics indicated that major white matter tracts were more robust to downsampling relative to cortical regions. Our study provides guidelines for new protocols in mouse models of neurological conditions, so as to achieve similar connectomes, while increasing efficiency.

Indexed as

brainconnectivity (graph theory)diffusion imagingmouse modelMRIneurodegenerative diseases

Identifiers

PMID33928076
PMCPMC8081353
OpenAlexW3014324543

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.