Colin Buchanan PhD

Colin Buchanan


Research Interests

My main research interests lie in neuroimaging and brain connectivity. I am currently combining whole-brain tractography techniques with network analysis using both structural and diffusion MRI data. The aim is to characterise the integrity of white matter in the brain and to assess network connectivity between cortical regions in healthy volunteers and potentially in motor neurone disease, normal ageing and disorders affecting connectivity.

I also have an interest in computational neuroscience, neural networks, functional neuroimaging, bioinformatics, machine learning and computer science.

Publications:
2014
  Test-retest reliability of structural brain networks from diffusion MRI
Buchanan, C, Pernet, CR, Gorgolewski, KJ, Storkey, AJ & Bastin, ME 2014, 'Test-retest reliability of structural brain networks from diffusion MRI' NeuroImage, vol 86, pp. 231-243. DOI: 10.1016/j.neuroimage.2013.09.054
Structural brain networks constructed from diffusion MRI (dMRI) and tractography have been demonstrated in healthy volunteers and more recently in various disorders affecting brain connectivity. However, few studies have addressed the reproducibility of the resulting networks. We measured the test-retest properties of such networks by varying several factors affecting network construction using ten healthy volunteers who underwent a dMRI protocol at 1.5T on two separate occasions. Each T1-weighted brain was parcellated into 84 regions-of-interest and network connections were identified using dMRI and two alternative tractography algorithms, two alternative seeding strategies, a white matter waypoint constraint and three alternative network weightings. In each case, four common graph-theoretic measures were obtained. Network properties were assessed both node-wise and per network in terms of the intraclass correlation coefficient (ICC) and by comparing within- and between-subject differences. Our findings suggest that test-retest performance was improved when: 1) seeding from white matter, rather than grey; and 2) using probabilistic tractography with a two-fibre model and sufficient streamlines, rather than deterministic tensor tractography. In terms of network weighting, a measure of streamline density produced better test-retest performance than tract-averaged diffusion anisotropy, although it remains unclear which is a more accurate representation of the underlying connectivity. For the best performing configuration, the global within-subject differences were between 3.2% and 11.9% with ICCs between 0.62 and 0.76. The mean nodal within-subject differences were between 5.2% and 24.2% with mean ICCs between 0.46 and 0.62. For 83.3% (70/84) of nodes, the within-subject differences were smaller than between-subject differences. Overall, these findings suggest that whilst current techniques produce networks capable of characterising the genuine between-subject differences in connectivity, future work must be undertaken to improve network reliability.
General Information
Organisations: Edinburgh Imaging Facilities.
Authors: Buchanan, Colin, Pernet, Cyril R, Gorgolewski, Krzysztof J., Storkey, Amos J & Bastin, Mark E.
Keywords: (Connectome , Diffusion MRI , Human brain , Network , Test–retest , Tractography. )
Pages: 231-243
Publication Date: 1 Feb 2014
Publication Information
Category: Article
Journal: NeuroImage
Volume: 86
ISSN: 1053-8119
Original Language: English
DOIs: 10.1016/j.neuroimage.2013.09.054
2013
  The test-retest reliability of structural brain networks obtained from diffusion MRI
Buchanan, CR, Pernet, C, Gorgolewski, KJ, Storkey, A & Bastin, M 2013, 'The test-retest reliability of structural brain networks obtained from diffusion MRI' 19th Annual Meeting of the Organization for Human Brain Mapping, Seattle, Washington, United States, 16/06/13 - 20/06/13, .
General Information
Organisations: Edinburgh Imaging Facilities.
Authors: Buchanan, Colin R., Pernet, Cyril, Gorgolewski, Krzysztof J., Storkey, Amos & Bastin, Mark.
Publication Date: 2013
Publication Information
Category: Poster
Original Language: English
  Structural Brain Networks in Amyotrophic Lateral Sclerosis
Buchanan, C, Pettit, L, Bastin, M, Storkey, A & Abrahams, S 2013, 'Structural Brain Networks in Amyotrophic Lateral Sclerosis' 19th Annual Meeting of the Organization for Human Brain Mapping, Seattle, Washington, United States, 16/06/13 - 20/06/13, .
General Information
Organisations: Edinburgh Imaging Facilities.
Authors: Buchanan, Colin, Pettit, Lewis, Bastin, Mark, Storkey, Amos & Abrahams, Sharon.
Publication Date: Jun 2013
Publication Information
Category: Poster
Original Language: English
2012
  Quantifying the intra- and inter-subject variability of whole-brain structural networks from diffusion MRI
Buchanan, C, Gorgolewski, KJ, Pernet, C, Storkey, A & Bastin, M 2012, 'Quantifying the intra- and inter-subject variability of whole-brain structural networks from diffusion MRI' ISMRM 20th Annual Meeting & Exhibition, Melbourne, Australia, 5/05/12 - 11/05/12, .
Connectomics is a recent development in neuroscience that combines diffusion MRI (dMRI) and tractography with the analytical tools of network theory to investigate whole-brain connectivity [1]. Under this paradigm, segmented cortical areas (e.g. Brodmann areas) form the nodes of a network and tractography is used to construct a set of white matters tracts which form the connections of the network. Graph-theoretic measures may then be used to characterize topological patterns of connectivity [2]. Recent studies have demonstrated whole-brain network analysis in healthy volunteers [3,4]. However, whether the connectome approach can consistently reconstruct structural white matter networks and produce robust clinically useful metrics remains an open question. Here we measure the reproducibility of basic graph-theoretic measures obtained from dMRI data using a percentile bootstrap technique. Since these measures are an essential prerequisite for more complex analyses, such as ““small-world”” measures or the identification of network hubs, their reliability is crucial to the whole connectome approach.
General Information
Organisations: Edinburgh Imaging Facilities.
Authors: Buchanan, Colin, Gorgolewski, Krzysztof J., Pernet, Cyril, Storkey, Amos & Bastin, Mark.
Publication Date: May 2012
Publication Information
Category: Poster
Original Language: English
  Intuitive and efficient deployment of neuroimaging pipelines in clinical research with BRICpipe
Glatz, A, Buchanan, C, Valdes Hernandez, M, Bastin, M & Wardlaw, J 2012, 'Intuitive and efficient deployment of neuroimaging pipelines in clinical research with BRICpipe: Clinical neuroscience' 5th World INCF Neuroinformatics Congress, Munich, Germany, Germany, 10/09/12 - 12/09/12, .
In clinical research, neuroimaging pipelines are mainly used to combine the strength of single analysis tools and to accelerate the data analysis. Pipeline frameworks, such as Nipype[1] or LONI[2] (Table 1), should be a researcher’s first choice to implement pipelines. However, clinical researchers often prefer Bash[3] scripts due to their simplicity and because pipeline frameworks still require specialist knowledge for setup and use.
To encourage the use of pipeline frameworks in clinical research, we developed our own open source pipeline framework, BRICpipe. Here, we present BRICpipe’s design and some validation results to show how BRICpipe compares to Nipype and Bash scripts.
General Information
Organisations: Edinburgh Imaging Facilities.
Authors: Glatz, Andreas, Buchanan, Colin, Valdes Hernandez, Maria, Bastin, Mark & Wardlaw, Joanna.
Publication Date: Sep 2012
Publication Information
Category: Abstract
Original Language: English
  Whole-brain structural networks from diffusion MRI
Buchanan, C 2012, 'Whole-brain structural networks from diffusion MRI' 2nd UK Neuroinformatics Node Congress, Edinburgh, United Kingdom, 26/03/12 - 28/03/12, .
General Information
Organisations: Neuroinformatics DTC.
Authors: Buchanan, Colin.
Publication Date: Mar 2012
Publication Information
Category: Poster
Original Language: English

Projects:
Whole-brain tractography and network analysis in health and disease (PhD)