Brain cortical variability, software, and clinical implications
dc.contributor.advisor
Pernet, Cyril
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dc.contributor.advisor
Hernandez, Maria Valdes
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dc.contributor.author
Mikhael, Shadia S.
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dc.contributor.sponsor
other
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dc.date.accessioned
2018-11-05T11:07:33Z
dc.date.available
2018-11-05T11:07:33Z
dc.date.issued
2018-11-30
dc.description.abstract
It is essential to characterize and quantify naturally occurring morphometric changes in the
human brain when investigating the onset or progression of neurodegenerative disorders.
The aim of this thesis is to characterize the properties and measure the performance of
several popular automated magnetic resonance image analysis tools dedicated to brain
morphometry.
The thesis begins with an overview of morphometric analysis methods, followed by a
literature review focusing on cortical parcellation protocols. Our work identified unanimous
protocol weaknesses across all packages in particular issues when addressing cortical
variability.
The next chapters present a ground truth dataset and a dedicated software to analyse
manually parcellated data. The dataset (https://datashare.is.ed.ac.uk/handle/10283/2936)
includes 10 healthy middle-aged subjects, whose metrics we used as reference against
automated tools. To develop the ground truth dataset, we also present a manual parcellation
protocol (https://datashare.is.ed.ac.uk/handle/10283/3148) providing step-by-step
instructions for outlining three cortical gyri known to vary with ageing and dementia: the
superior frontal gyrus, the cingulate gyrus and the supramarginal gyrus. The software,
Masks2Metrics (https://datashare.is.ed.ac.uk/handle/10283/3018), was built in Matlab to
calculate cortical thickness, white matter surface area, and grey matter volume from 3D
binary masks. Characterizing these metrics allowed further understanding of the
assumptions made by software when creating and measuring anatomical parcels.
Next, we present results from processing the raw T1-weighted volumes in the latest versions
of several automated image analysis tools—FreeSurfer (versions 5.1 and 6.0),
BrainGyrusMapping, and BrainSuite (version 13a)— against our ground truth. Tool
repeatability for the same system was confirmed as multiple runs yielded identical results.
Compared to our ground truth, the closest results were generated by BrainGyrusMapping for
volume metrics and by FreeSurfer 6.0 for thickness and surface area metrics.
In conclusion, our work sheds light on the significance of clearly detailed parcellation
protocols and accurate morphometric tools due to the implications that they both will have.
We therefore recommend extra caution when selecting image analysis tools for a study, and
the use of independent publicly available ground truth datasets and metrics tools to assist
with the selection process.
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dc.identifier.uri
http://hdl.handle.net/1842/33210
dc.language.iso
en
dc.publisher
The University of Edinburgh
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dc.relation.hasversion
Bastin, M., Wardlaw, J., Pernet, C., & Mikhael, S. (2017). Edinburgh_NIH10. Edinburgh_NIH10.
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dc.relation.hasversion
Mikhael, S., & Gray, C. (2017). Masks2Metrics. Retrieved from https://github.com/Edinburgh-Imaging/Masks2Metrics
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dc.relation.hasversion
Mikhael, S., & Gray, C. (2018b). Masks2Metrics (M2M): A Matlab Toolbox for Gold Standard Morphometrics. Journal of Open Source Software, 3(22). doi:10.21105/joss.00436
en
dc.relation.hasversion
Mikhael, S., Mair, G., & Pernet, C. (2018). A Manual Segmentation Protocol for Cortical Gyri. In C. f. C. B. S. a. E. I. University of Edinburgh. College of Medicine and Veterinary Medicine (Ed.), A Manual Segmentation Protocol for Cortical Gyri. Edinburgh: Datashare.
en
dc.relation.hasversion
Mikhael, S., Hoogendoorn, C., Valdes-Hernandez, M., & Pernet, C. (2018). A critical analysis of neuroanatomical software protocols reveals clinically relevant differences in parcellation schemes. NeuroImage, 170, 348-364. doi:10.1016/j.neuroimage.2017.02.082
en
dc.relation.hasversion
Mikhael, S., & Gray, C. (2018a). Masks2Metrics (M2M) 1.0: a Matlab tool for region-of-interest metrics (Version 1.0) [software]. University of Edinburgh. Centre for Clinical Brain Sciences: Datashare
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dc.relation.hasversion
Mikhael, S., & Pernet, C. (2018). Morphometric data for Edinburgh_NIH10 dataset. Morphometric data for Edinburgh_NIH10 dataset.
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dc.rights.embargodate
2019-11-30
dc.subject
cortical
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dc.subject
variability
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dc.subject
morphometry
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dc.subject
grey matter thickness
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dc.subject
grey matter volume
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dc.subject
white matter surface area
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dc.title
Brain cortical variability, software, and clinical implications
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dc.type
Thesis or Dissertation
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dc.type.qualificationlevel
Doctoral
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dc.type.qualificationname
PhD Doctor of Philosophy
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dcterms.accessRights
Restricted Access
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