Colin Buchanan
Research Associate
- Psychology
- School of Philosophy, Psychology and Language Sciences
Contact details
- Tel: 0131 650 3453
- Email: Colin.Buchanan@ed.ac.uk
Address
- Street
-
Room F1, Psychology Building
- City
- 7 George Square, Edinburgh
- Post code
- EH8 9JZ
Background
Colin is based in the Lothian Birth Cohorts team within the Psychology Department. He works with Lothian Birth Cohort 1936 and UK Biobank neuroimaging data as part of a 5-year NIH grant to investigate the brain's structural connectivity in relation to cognitive function, health and genes.
Research summary
Colin's field of work is connectomics, the study of the connectome, which seeks to map the connections of the brain as a network. Using imaging data obtained from diffusion MRI and whole-brain tractography, connectome methods are used to map the collective wiring of many billions of axonal nerve fibres across the brain. The connectome approach provides tools to ask neuroscientific questions concerned with healthy and pathological brain organisation. This approach may also provide evidence on how cerebral white matter underlies cognitive function.
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White matter, cognition and psychotic-like experiences in UK Biobank
(10 pages)
In:
Psychological Medicine
DOI: https://doi.org/10.1017/S0033291721004244
Research output: Contribution to Journal › Article (E-pub ahead of print) -
Comparison of structural MRI brain measures between 1.5 and 3T: Data from the Lothian Birth Cohort 1936
In:
Human Brain Mapping, pp. 3905-3921
DOI: https://doi.org/10.1002/hbm.25473
Research output: Contribution to Journal › Article (Published) -
Three major dimensions of human brain cortical ageing in relation to cognitive decline across the eighth decade of life
(12 pages)
In:
Molecular Psychiatry
DOI: https://doi.org/10.1038/s41380-020-00975-1
Research output: Contribution to Journal › Article (E-pub ahead of print) -
Identification of plasma proteins relating to brain neurodegeneration and vascular pathology in cognitively normal individuals
In:
Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring
DOI: https://doi.org/10.1002/dad2.12240
Research output: Contribution to Journal › Article (Published) -
Pipeline comparisons of convolutional neural networks for structural connectomes: predicting sex across 3,152 participants
DOI: https://doi.org/https://doi-org.ezproxy.is.ed.ac.uk/10.1109/EMBC44109.2020.9175596
Research output: Contribution to Conference › Paper (Published)