The publisher regrets that due to an error the full text of Appendix A
was missing in the original publication. The missing text is included
below.
The publisher would like to apologise for any inconvenience caused

Cam-Can

Correia, Marta M

Davis, Simon W

Griffiths, John

Henson, Richard N

Kievit, Rogier A

Rogier A. Kievit

Simon W. Davis

John Griffiths

Marta M. Correia

null Cam-CAN

Richard N. Henson

Crossref

Neuropsychologia

Erratum to “A watershed model of individual differences in fluid intelligence” [Neuropsychologia 91 (2016) 186–198]

Apollo (Cambridge)

Contents lists available at ScienceDirectNeuropsychologiajournal homepage: www.elsevier.com/locate/neuropsychologiaErratumErratum to “A watershed model of individual diﬀerences in ﬂuidintelligence” [Neuropsychologia 91 (2016) 186–198]Rogier A. Kievita,⁎, Simon W. Davisb,c, John Griﬃthsb,d, Marta M. Correiaa, Cam-CANe,1,Richard N. Hensonaa MRC Cognition and Brain Sciences Unit, 15 Chaucer Rd, Cambridge CB27EF, United Kingdomb Department of Psychology, University of Cambridge, Downing Street, Cambridge CB23EB, United Kingdomc Center for Cognitive Neuroscience, Duke University, Durham, NC 27708, United Statesd Rotman Research Institute, Baycrest, Toronto, Ontario, Canada M6A2E1e Cambridge Centre for Ageing and Neuroscience (Cam-CAN), University of Cambridge and MRC Cognition and Brain Sciences Unit, Cambridge, United KingdomThe publisher regrets that due to an error the full text of Appendix Awas missing in the original publication. The missing text is includedbelow.The publisher would like to apologise for any inconvenience caused.Appendix AEstimating WM ROIsDiﬀusion-weighted images (DWI) were acquired at the MRCCognition and Brain Sciences Unit, using a 3T Siemens TIM Trio MRIscanner, and a 32-channel head coil. A twice-refocused spin-echo se-quence was used to minimise eddy-currents, with 30 uniformly spacedgradient directions for each of two b-values (1000 and 2000 s/mm2),and three non-diﬀusion weighted images (b-value = 0). Other imagingparameters were: TR = 9100 ms, TE = 104 ms, 2 × 2 × 2 mm3 re-solution, FoV 192 × 192 mm2, 66 axial slices and GRAPPA accelerationfactor of 2. A structural MPRAGE was also acquired for each participant(see Shafto et al. (2014) for sequence details). Traditionally, DWI datais motion corrected at the postacquisition level by using image regis-tration techniques to co-register each diﬀusion-weighted image to theﬁrst acquired b = 0 image. However, as discussed in Ben-Amitay et al.(2012), when high b-values are used, this technique will fail to correctfor distortions and motion, and may introduce other artefacts. For thisreason, we did not apply registration-based motion correction to theDWI data in this study. Detection and exclusion of outliers was per-formed at the analysis level to avoid including datasets aﬀected byother artefacts and distortions. All pre-processing and modelling of MRIdata was performed using a combination of functions from FSL version5.0.8 (Jenkinson et al., 2012), SPM12 (http://www.ﬁl.ion.ucl.ac.uk/spm/software/spm12/), and custom scripts written in C and Matlab,integrated in the Automatic Analysis (aa) package (Cusack et al., 2014).After removal of non-brain tissue, a non-linear diﬀusion tensor modelwas applied to the DWI data. Non-linear ﬁtting of the diﬀusion tensorprovides better noise modelling than standard linear model ﬁtting, re-sulting in more accurate and un-biased estimates of the diﬀusion tensorand its diﬀerent metrics (Jones and Basser, 2004). The diﬀusion tensor’seigensystem was used to compute the fractional anisotropy (FA) at eachvoxel. The FA maps were then spatially normalised into a standardstereotactic space as follows: ﬁrstly, the average of the three b = 0images for each subject was coregistered to their MPRAGE; secondly,the MPRAGE images were coregistered across participants to a sample-speciﬁc template using DARTEL (Ashburner, 2007), and the transfor-mations so derived were used to warp each participant’s images intostandard MNI space, including their FA maps. The resulting FA imagesin MNI space were smoothed with a 1 mm FWHM Gaussian kernel toreduce residual interpolation errors.ReferencesAshburner, J., 2007. A fast diﬀeomorphic image registration algorithm. NeuroImage 38(1), 95–113. http://dx.doi.org/10.1016/j.neuroimage.2007.07.007.Ben-Amitay, S., Jones, D.K., Assaf, Y., 2012. Motion correction and registration of high b-value diﬀusion weighted images. Magn. Reson. Med. 67 (6), 1694–1702. http://dx.doi.org/10.1002/mrm.23186.Cusack, R., Vicente-Grabovetsky, A., Mitchell, D.J., Wild, C.J., Auer, T., Linke, A.C.,Peelle, J.E., 2014. Automatic analysis (aa): eﬃcient neuroimaging workﬂows andparallel processing using Matlab and XML. Front. Neuroinform. 8, 90. http://dx.doi.org/10.3389/fninf.2014.00090.Jenkinson, M., Beckmann, C.F., Behrens, T.E.J., Woolrich, M.W., Smith, S.M., 2012. FSL.NeuroImage 62 (2), 782–790. http://dx.doi.org/10.1016/j.neuroimage.2011.09.015.Jones, D.K., Basser, P.J., 2004. “Squashing peanuts and smashing pumpkins”: how noisedistorts diﬀusion-weighted MR data. Magn. Reson. Med. 52 (5), 979–993. http://dx.doi.org/10.1002/mrm.20283.Shafto, M.A., Tyler, L.K., Dixon, M., Taylor, J.R., Rowe, J.B., Cusack, R., Calder, A.J.,Marslen-Wilson, W.D., Duncan, J., Dalgleish, T., Henson, R.N., Brayne, C., Matthews,F.E., Cam-CAN, Matthews, F.E., 2014. The Cambridge Centre for Ageing andNeuroscience (Cam-CAN) study protocol: a cross-sectional, lifespan, multidisciplinaryexamination of healthy cognitive ageing. BMC Neurol. 14, 1–25. http://dx.doi.org/10.1186/s12883-014-0204-1.http://dx.doi.org/10.1016/j.neuropsychologia.2017.09.032DOI of original article: http://dx.doi.org/10.1016/j.neuropsychologia.2016.08.008⁎ Corresponding author.1 www.cam-can.com.E-mail address: Rogier.Kievit@mrc-cbu.cam.ac.uk (R.A. Kievit).Neuropsychologia 106 (2017) 417Available online 06 October 20170028-3932/ © 2017 MRC CBSU. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/BY/4.0/).MARK

Erratum to "A watershed model of individual differences in fluid intelligence" [Neuropsychologia 91 (2016) 186-198].

Erratum to "A watershed model of individual differences in fluid intelligence" [Neuropsychologia 91 (2016) 186-198].

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