Supplemental Methods Methods S1




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Supplemental Methods
Methods S1

Diffusion tensor imaging: data preprocessing and fiber tractography (methodological description, also described previously [1])
Preprocessing was done with FMRIB Software Library (FSL) Version 4.1.8 [2] (www.fmrib.ox.ac.uk/fsl) and comprised the following steps: 1) segregation of brain tissue from non-brain tissue using the Brain Extraction Tool [3]; 2) Eddy current and head movement correction using EDDYCORRECT from FMRIB’s Diffusion Toolbox [2]; 3) rotation of the gradients according to the corrected parameters from step 2); 4) local fitting of diffusion tensors and construction of individual FA maps using DTIFIT from FMRIB’s Diffusion Toolbox [2].

For fiber tracking, Diffusion Toolkit 0.6.1 and TrackVis 0.5.1 were used [4] (www.trackvis.org). The preprocessed data from FSL were further processed with Diffusion Toolkit. For each subject, the diffusion tensors were estimated according to the corrected gradients. Deterministic fiber tracking was performed with the “brute-force” approach [5], an automatic procedure commonly used to reconstruct fibers across the whole WM by tracking fibers from each voxel in the brain. The fiber assignment continuous tracking (FACT) algorithm [6] was used. Accordingly, fibers were reconstructed by TrackVis along the principal eigenvector of each voxel’s diffusion tensor. Tracking termination criteria were angle > 45° and FA < 0.2 [7] (individual FA map derived from FSL’s DTIFIT was used as mask image in Diffusion Toolkit). Fiber tracking was performed successively in each subject’s native space. Color-coded FA maps derived from the principal eigenvector of the diffusion tensor in each voxel were used for region-of-interest (ROI) drawing in TrackVis. ROIs were drawn large-sized to include the entirety of the tract of interest and avoid false-negative fibers [8] (see also figure 2). All fiber tracts were obtained through a two-ROI approach (seed ROI and target ROI) with logical AND concatenation [9, 10] of the two ROIs, such that only fibers that passed both ROIs were included in the reconstructed tract. Obviously spurious fibers were removed from the fiber tract by using an additional avoidance ROI (logical NOT operation) [9]. For the UF, both the seed and the target ROI was placed in the same coronal slice where the anterior-posterior fibers (coded in green) of the frontal and the temporal lobe were visible at the most posterior point (see figure 2A for illustration of the ROI placement and tractography examples for the UF, see also [9]). For the IFOF, the seed ROI was placed in the occipital lobe according to Wakana and colleagues [9]. The target ROI was placed at the densest portion of the fiber bundle projecting anteriorly (coded in green, anterior floor of the external capsule [10]), typically located in the coronal slice that dissects the middle of the corpus callosum body (see figure 2B for illustration of the ROI placement and tractography examples for the IFOF). Each tract was reconstructed in both hemispheres, and tracking was randomly performed either first in the left or in the right hemisphere in each subject. After tractography, each individual tract was visually inspected for plausibility with regard to its structure based on general anatomical knowledge and previously published tractography studies [9-11]. For each tract, any voxel touched by a fiber was counted by TrackVis. As such, volume values were obtained by accumulating all voxels belonging to the respective tract.



Methods S2

Automatic parcellation of subcortical structures and estimation of intra-cranial volume
Volumetric segmentation was performed with the Freesurfer image analysis suite (Version 5.1.0), which is documented and freely available for download online (http://surfer.nmr.mgh.harvard.edu/). The technical details of these procedures are described in prior publications [12-23]). Briefly, this processing includes motion correction and averaging of multiple volumetric T1-weighted images (when more than one is available), removal of non-brain tissue using a hybrid watershed/surface deformation procedure [19], automated Talairach transformation, segmentation of the subcortical white matter and deep gray matter volumetric structures (including amygdala, hippocampus, thalamus, caudate, putamen, pallidum, nucleus accumbens, ventricles) [20, 21] intensity normalization [24], tessellation of the gray matter white matter boundary, automated topology correction [22, 25], and surface deformation following intensity gradients to optimally place the gray/white and gray/cerebrospinal fluid borders at the location where the greatest shift in intensity defines the transition to the other tissue class [12, 13, 23]. Freesurfer morphometric procedures have been demonstrated to show good test-retest reliability across scanner manufacturers and across field strengths [17].

The procedure for intra-cranial volume estimation automatically assigns a neuroanatomical label to each voxel in the T1-weighted scan, a label that is based on probabilistic information automatically estimated from a manually labeled training set [26]. The technique has previously been shown to be comparable in accuracy to manual labeling [26,27]. Intra-cranial volume was calculated by the use of an atlas-based normalization procedure, where the atlas-scaling factor is used as a proxy for intra-cranial volume. It has been shown that this estimated intra-cranial volume correlates highly with manually derived measurements of intra-cranial volume [27].



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27. Buckner RL, Head D, Parker J, Fotenos AF, Marcus D, Morris JC, Snyder AZ: A unified approach for morphometric and functional data analysis in young, old, and demented adults using automated atlas-based head size normalization: reliability and validation against manual measurement of total intracranial volume. NeuroImage 2004, 23(2):724-738.



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