Preprocessing

For the articles summarized in this thesis we used readily preprocessed data provided by the UKB. The preprocessing pipeline for that data is described in Alfaro-Almagro et al.¹ and relied heavily on functionality included in the FSL toolbox^2,3. The pipeline included motion corrected using MC-FLIRT⁴, grand-mean intensity normalization, high-pass filtering through Gaussian-weighted least-squares straight line fitting, EPI unwarping and GDC unwarping. A number of structured artifacts were removed using ICA and FIX^5,6. The FIX classifier was trained manually on 40 UKB datasets. Finally, the preprocessing pipeline included two group ICAs using the MELODIC tool⁷ which decomposed the data into 25 and 100 independent components. In this project we used only the data from the 25 independent components.

1.

Alfaro-Almagro F, Jenkinson M, Bangerter NK, Andersson JLR, Griffanti L, Douaud G, et al. Image processing and Quality Control for the first 10,000 brain imaging datasets from UK Biobank. NeuroImage [Internet]. 2018 Feb 1;166:400–24. Available from: https://www.sciencedirect.com/science/article/pii/S1053811917308613

2.

Jenkinson M, Beckmann CF, Behrens TEJ, Woolrich MW, Smith SM. FSL. NeuroImage [Internet]. 2012 Aug 15;62(2):782–90. Available from: https://www.sciencedirect.com/science/article/pii/S1053811911010603

3.

Smith SM, Jenkinson M, Woolrich MW, Beckmann CF, Behrens TEJ, Johansen-Berg H, et al. Advances in functional and structural MR image analysis and implementation as FSL. Neuroimage. 2004;23 Suppl 1:S208–219.

4.

Jenkinson M, Bannister P, Brady M, Smith S. Improved optimization for the robust and accurate linear registration and motion correction of brain images. Neuroimage. 2002 Oct;17(2):825–41.

5.

Griffanti L, Salimi-Khorshidi G, Beckmann CF, Auerbach EJ, Douaud G, Sexton CE, et al. ICA-based artefact removal and accelerated fMRI acquisition for improved resting state network imaging. Neuroimage. 2014 Jul 15;95:232–47.

6.

Salimi-Khorshidi G, Douaud G, Beckmann CF, Glasser MF, Griffanti L, Smith SM. Automatic denoising of functional MRI data: Combining independent component analysis and hierarchical fusion of classifiers. Neuroimage. 2014 Apr 15;90:449–68.

7.

Beckmann CF, Smith SM. Probabilistic independent component analysis for functional magnetic resonance imaging. IEEE Trans Med Imaging. 2004 Feb;23(2):137–52.