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Multi-Shell Hybrid Diffusion Imaging (HYDI) at 7 Tesla in TgF344-AD Transgenic Alzheimer Rats

Daianu, Madelaine and Jacobs, Russell E. and Weitz, Tara M. and Town, Terrence C. and Thompson, Paul M. (2015) Multi-Shell Hybrid Diffusion Imaging (HYDI) at 7 Tesla in TgF344-AD Transgenic Alzheimer Rats. PLoS ONE, 10 (12). Art. No. e0145205. ISSN 1932-6203. PMCID PMC4687716. https://resolver.caltech.edu/CaltechAUTHORS:20160104-072848775

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Abstract

Diffusion weighted imaging (DWI) is widely used to study microstructural characteristics of the brain. Diffusion tensor imaging (DTI) and high-angular resolution imaging (HARDI) are frequently used in radiology and neuroscience research but can be limited in describing the signal behavior in composite nerve fiber structures. Here, we developed and assessed the benefit of a comprehensive diffusion encoding scheme, known as hybrid diffusion imaging (HYDI), composed of 300 DWI volumes acquired at 7-Tesla with diffusion weightings at b = 1000, 3000, 4000, 8000 and 12000 s/mm^2 and applied it in transgenic Alzheimer rats (line TgF344-AD) that model the full clinico-pathological spectrum of the human disease. We studied and visualized the effects of the multiple concentric “shells” when computing three distinct anisotropy maps–fractional anisotropy (FA), generalized fractional anisotropy (GFA) and normalized quantitative anisotropy (NQA). We tested the added value of the multi-shell q-space sampling scheme, when reconstructing neural pathways using mathematical frameworks from DTI and q-ball imaging (QBI). We show a range of properties of HYDI, including lower apparent anisotropy when using high b-value shells in DTI-based reconstructions, and increases in apparent anisotropy in QBI-based reconstructions. Regardless of the reconstruction scheme, HYDI improves FA-, GFA- and NQA-aided tractography. HYDI may be valuable in human connectome projects and clinical research, as well as magnetic resonance research in experimental animals.


Item Type:Article
Related URLs:
URLURL TypeDescription
http://dx.doi.org/10.1371/journal.pone.0145205DOIArticle
http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0145205PublisherArticle
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4687716/PubMed CentralArticle
https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/D0OVVBOrganizationData Set
ORCID:
AuthorORCID
Jacobs, Russell E.0000-0002-1382-8486
Additional Information:© 2015 Daianu et al. December 18, 2015. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Algorithm development and image analysis for this study were funded, in part, by grants to PT from the NIBIB (R01 EB008281, R01 EB008432) and by the NIA, NIBIB, NIMH, and the National Library of Medicine (AG016570, AG040060, EB01651, MH097268, LM05639 to PT) and by an NINDS grant to TT (R01 NS076794). Data collection and sharing for this project was funded by NIH Grant R01 AG034499-05. NIBIB & the Beckman Institute at Caltech provided essential support for the scanner & personnel (NIBIB EB000993). TT and TMW were supported by an NIH/NINDS grant (5R01NS076794-05). This work was also supported in part by a Consortium grant (U54 EB020403) from the NIH Institutes contributing to the Big Data to Knowledge (BD2K) Initiative and R01 EB008432. Algorithm development and image analysis for this study were funded, in part, by grants to PT from the NIBIB (R01 EB008281, R01 EB008432) and by the NIA, NIBIB, NIMH, and the National Library of Medicine (AG016570, AG040060, EB01651, MH097268, LM05639 to PT) and by an NINDS grant to TT (R01 NS076794). Data collection and sharing for this project was funded by NIH Grant R01 AG034499-05. NIBIB & the Beckman Institute at Caltech provided essential support for the scanner & personnel (NIBIB EB000993). This work was also supported in part by a Consortium grant (U54 EB020403) from the NIH Institutes contributing to the Big Data to Knowledge (BD2K) Initiative and R01 EB008432. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. The data set for this paper can be found at https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/D0OVVB. Author Contributions: Conceived and designed the experiments: MD REJ TCT PMT. Performed the experiments: MD REJ TMW TCT PMT. Analyzed the data: MD. Contributed reagents/materials/analysis tools: MD REJ TCT PMT. Wrote the paper: MD PMT.
Funders:
Funding AgencyGrant Number
National Institute of Biomedical Imaging and BioengineeringR01 EB008281
National Institute of Biomedical Imaging and BioengineeringR01 EB008432
National Institute on AgingUNSPECIFIED
National Library of MedicineAG016570
National Library of MedicineAG040060
National Library of MedicineEB01651
National Library of MedicineMH097268
National Library of MedicineLM05639
National Institute of Neurological Disorders and Stroke (NINDS)R01 NS076794
NIHR01 AG034499-05
Caltech Beckman InstituteUNSPECIFIED
National Institute of Biomedical Imaging and BioengineeringEB000993
NIHU54 EB020403
NIHR01 EB008432
National Institute of Mental Health (NIMH)UNSPECIFIED
Issue or Number:12
PubMed Central ID:PMC4687716
Record Number:CaltechAUTHORS:20160104-072848775
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20160104-072848775
Official Citation:Daianu M, Jacobs RE, Weitz TM, Town TC, Thompson PM (2015) Multi-Shell Hybrid Diffusion Imaging (HYDI) at 7 Tesla in TgF344-AD Transgenic Alzheimer Rats. PLoS ONE 10(12): e0145205. doi:10.1371/journal.pone.0145205
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:63310
Collection:CaltechAUTHORS
Deposited By: Ruth Sustaita
Deposited On:04 Jan 2016 18:44
Last Modified:03 Oct 2019 09:27

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