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«Published in final edited form as: Brain and Language, 2013 DOI: 10.1016/j.bandl.2013.03.001 Dorsal and ventral pathways in language development Jens ...»

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Supplementary content Dorsal and ventral pathways in language development Jens Brauer, Alfred Anwander, Daniela Perani & Angela D. Friederici Supplementary methods The newborns’ data of the present study were obtained on a different scanner than the children’s and adults’ data. In order to ensure the reliability of the observed group differences in FA, we compared the newborns’ data with data from a second control group of adolescents and young adults (N = 15, age = 18.5, range = 15 to 22) obtained on the same scanner with a very similar protocol adapted for adults (FOV 231 x 136 x 240, resolution 112 x 88, 59 slices, TE 58ms, TR 9.8s, 35 gradient directions). Tracking results were obtained for this control group according to the methods described in the main text. As for the original adult sample, all fiber pathways of interest were tracked. Compared to our original sample of adults (mean FA = 0.462), this younger sample showed slightly smaller FA values (mean FA = 0.437) (see Suppl. Fig. 1). Statistical comparison was conducted for the control vs. infant data obtained at the same scanner. A two (groups: infants, controls) by four (tracts: dorsal pathway D1: AF pmc, ventral pathways: IFOF lat, IFOF dors, IFOF orb) repeated measures ANOVA confirmed our original findings with significant main effects for group (F(1,32) = 116.4, p.001) and tract (F(3,96) = 51.5, p.001) and a significant interaction F(3,96) = 36.9, p.001). This is consistent with the results of the original analysis.





Furthermore, we compared signal-to-noise ratio (SNR) between newborns’, children’s and adults’ data.

SNR was extracted for white matter for the averaged B1000 image as described in Blank, Anwander, & von Kriegstein (2011). SNR was measured as the mean signal in the white matter divided by the standard deviation in a background region (free from ghosting or blurring artifacts). Values for three typical datasets were: adult: SNRmean b1000 = 49.5, child: SNR mean b1000 = 52.6, and newborn: SNR mean b1000 =

34.9. SNR was computed in the mean b1000 to take into account the variable signal intensities in the different diffusion gradients. In the mean image, the SNR increases approximately by the square root of the number of averages. Considering 60 directions in the adult and the child datasets and 21 directions in the newborn datasets, the estimated SNR values for a single b1000 image are rather comparable (adults: SNR single b1000 = 6.4, child: SNR single b1000 = 6.8, newborn: SNR single b1000 = 7.6).

The exact minimum of SNR for robust fiber tractography is difficult to determine. Jones and Basser (2004) proposed a minimum of 3 to avoid problems associated with the rectified noise floor in the estimation of the diffusion model. This corresponds to a SNR in the b0 image of 3/0.37 = 8.1 (Jones, Knösche, & Turner, 2012). Smith and colleagues (2007) proposed a minimum SNR in the b0 image of 15 for tractskeleton based analysis of derived measures like FA. Tractography has higher requirements on data quality than voxel based analysis to prevent the propagation of local errors and Fillard and colleagues (2011) recommend to use images with high SNR in order to obtain reliable tracking results. In this study, different tractography algorithms were evaluated with SNR in the b0 image of 15.8 (low) and 22.6 (high) and an SNR in the mean b 1500 image of 2.6 (low) and 17.6 (high). All mentioned recommendations are by far exceeded by every dataset in our samples. There is sufficiently high SNR in the present data for each group in order to obtain reliable high quality fiber tracking and evaluation of diffusion parameters.

Also our single subject trackings (see Suppl. Fig. 2) support the quality of the results.

Another limitation of the study is a potential effect of the different voxel sizes used for newborns (1.4 x

1.4 x 2.0 mm) compared to children and adults (1.7 x 1.7 x 1.7mm). FA had been reported to be affected by voxel size (Oouchi et al., 2007). This study reported smaller FA in the SLF by 0.02 when increasing the voxel size by a factor of 3. However, this was reported for rather large differences in voxel size (2 mm vs.

6 mm), while the difference in the voxel size in the present analysis is much smaller. The large FA difference of 0.2 in the AF between newborns and adults cannot be explained by the variable voxel size.

Supplementary figures

Supplementary Figure 1: Mean FA values for the dorsal pathway D1 (AFpmc) and the three components of the ventral pathway (the lateral portion of the IFOF: IFOFlat, the dorsal portion of the IFOF: IFOFdors, and the orbital portion of the IFOF: IFOForb) for infants and the older control group obtained at the same scanner. Infants show lower FA values for all tracts, particularly for the AFpmc. This comparison confirms the results from the main analysis (see results).

Supplementary Figure 2: Randomly selected samples of single-subject tracking results for the dorsal connection for three individual datasets from of the three groups (newborn infants, children, adults) as well as from the older control group obtained at the same scanner as the newborn’s data. In none of the newborns the D2 bundle (blue) connecting to the inferior frontal gyrus is traceable, while the D1 connection (yellow) to the premotor cortex can be tracked. Conversely, for all children and adults as well as the controls, both dorsal connections can be tracked. ROI-boxes for fiber selection in the frontal and temporal lobe are shown. They were positioned to segment the long segment of the arcuate fascicle according to Catani and colleagues (2005).

Supplementary Figure 3: A freely rotatable 3D view of tracking results for  newborns for the dorsal pathway D1 (yellow), and for the ventral pathways  V1 (green) and V2 (orbital branch: blue, dorsal branch: red). Every 1 out of  10 streamlines for each tract is represented. The 3D model in this figure  was integrated in the portable document format (PDF) using SimLab  Composer 2013 (SimLab Soft., Amman, Jordan) and requires the use of a  compatible PDF reader (eg. Adobe Reader 9).

Supplementary Figure 4: A freely rotatable 3D view of tracking results for  children for the dorsal pathways D1 (yellow) and D2 (blue), and for the  ventral pathways V1 (green) and V2 (orbital branch: blue, dorsal branch:  red). Every 1 out of 10 streamlines for each tract is represented. The 3D  model in this figure was integrated in the portable document format (PDF)  using SimLab Composer 2013 (SimLab Soft., Amman, Jordan) and requires  the use of a compatible PDF reader (eg. Adobe Reader 9).

Supplementary Figure 5: A freely rotatable 3D view of tracking results for  adults for the dorsal pathways D1 (yellow) and D2 (blue), and for the  ventral pathways V1 (green) and V2 (orbital branch: blue, dorsal branch:  red). Every 1 out of 10 streamlines for each tract is represented. The 3D  model in this figure was integrated in the portable document format (PDF)  using SimLab Composer 2013 (SimLab Soft., Amman, Jordan) and requires  the use of a compatible PDF reader (eg. Adobe Reader 9).

References Blank, H., Anwander, A., & von Kriegstein, K. (2011). Direct Structural Connections between Voice- and Face-Recognition Areas. The Journal of Neuroscience, 31(36), 12906-12915.

Catani, M., Jones, D. K., & ffytche, D. H. (2005). Perisylvian language networks of the human brain.

Annals of Neurology, 57(1), 8-16.

Fillard, P., Descoteaux, M., Goh, A., Gouttard, S., Jeurissen, B., Malcolm, J., et al. (2011). Quantitative evaluation of 10 tractography algorithms on a realistic diffusion MR phantom. NeuroImage, 56(1), 220Jones, D. K., & Basser, P. J. (2004). “Squashing peanuts and smashing pumpkins”: How noise distorts diffusion-weighted MR data. Magnetic Resonance in Medicine, 52(5), 979-993.

Jones, D. K., Knösche, T. R., & Turner, R. (2012). White matter integrity, fiber count, and other fallacies:

The do's and don'ts of diffusion MRI. NeuroImage. doi: 10.1016/j.neuroimage.2012.06.081.

Oouchi, H., Yamada, K., Sakai, K., Kizu, O., Kubota, T., Ito, H., et al. (2007). Diffusion anisotropy measurement of brain white matter is affected by voxel size: Underestimation occurs in areas with crossing fibers. American Journal of Neuroradiology, 28(6), 1102-1106.

Smith, S. M., Johansen-Berg, H., Jenkinson, M., Rueckert, D., Nichols, T. E., Miller, K. L., et al. (2007).

Acquisition and voxelwise analysis of multi-subject diffusion data with Tract-Based Spatial Statistics.

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