White Matter Asymmetry During Development Using Diffusion Kurtosis Imaging
Xiang Gao1, Farida Grinberg1,2, Ezequiel Farrher1, Fei Li1, Eileen Oberwelland3,4, Irene Neuner1,5,6, Kerstin Konrad4,6,7, and N.Jon. Shah1,2,6

1Institute of Neuroscience and Medicine - 4, Forschungszentrum Juelich GmbH, Juelich, Germany, 2Department of Neurology, Faculty of Medicine, RWTH Aachen University, Aachen, Germany, 3Translational Brain Research in Psychiatry and Neurology, Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, RWTH Aachen University, Aachen, Germany, 4Institute of Neuroscience and Medicine - 3, Forschungszentrum Juelich GmbH, Juelich, Germany, 5Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen University, Aachen, Germany, 6JARA - BRAIN, Translational Medicine, Juelich, Germany, 7Child Neuropsychology Section, Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, RWTH Aachen University, Aachen, Germany

Synopsis

We compare changes in the white matter asymmetry index in conventional fractional anisotropy (FA) and other diffusion kurtosis imaging (DKI) metrics in adults and children. For some fibres, such as cingulate gyrus, hippocampus and superior longitudinal fasciculus, other DKI parameters show significant asymmetry where FA fails. When compared to adults, children showed more laterality in cingulate gyrus, superior longitudinal fasciculus and superior longitudinal fasciculus in temporal parts, which indicate that the degree of asymmetry in these fibres is higher during childhood.

Target audience

Neuroscientists interested in the study of white matter brain asymmetry.

Purpose

The brain hemispheres of the healthy human are asymmetric in terms of function and structure1. During normal development, the degree of asymmetry varies for the different brain areas as found, in particular, by diffusion tensor imaging (DTI)2. Thus far, to explore the brain microstructural asymmetry, researchers have mainly focused on parameters such as fractional anisotropy (FA) using conventional DTI. The aim of this study is to investigate what complimentary information can be obtained regarding brain microstructural asymmetry using diffusion kurtosis imaging (DKI)3, a more advanced extension of DTI that allows one to assess the level of diffusional non Gaussianity. The work has been performed on a group of adults and a group of children. Moreover, we investigated the differences between these two groups in order to elucidate the change of asymmetry during development.

Methods

Healthy, right-handed adults (N=20) and children (N=21) underwent DKI in a 3T MRI scanner (Siemens MAGNETOM Tim-Trio). A double-refocused diffusion-weighted spin-echo EPI sequence was used with three b-values = 0, 1000 and 2800 s/mm2 and 30 gradient directions. Nonlinear coregistration to the FA map from the John Hopkins University (JHU)4 was carried out using in-house Matlab scripts. Evaluation of the following DKI scalar parameters was performed with the help of the ExploreDTI toolkit5: FA, MD, axial (AD) and radial diffusivities (RD), kurtosis anisotropy (KA), mean (MK), axial (AK) and radial kurtoses (RK). Average over anatomical regions of the DKI metrics was performed using the JHU atlas (18 regions-of-interest (ROI) - 9 left and 9 right, forceps major and minor were excluded). The asymmetry index (AI) was evaluated for all DKI parameters and all anatomies as AI = 2(L-R)/(L+R). We used a one-sample t-test to examine hemispheric differences in each group and applied Bonferroni corrections for multiple comparisons. Then, in order to assess the asymmetry during development, we compared the AI in adults and children using a two-sample t-test for those anatomical regions where both group showed significant laterality.

Results

Leftward (L>R) FA asymmetry in Cg and rightward (R>L) FA asymmetry in Ch, SLF, UF and SLF_temp were observed for both adults and children groups. Besides, rightward FA asymmetry in CST was found for children only. In Cg, Ch and SLF the AI of AK showed values around two times higher than AI of FA for both groups. In ATR, IFOF and ILF, FA asymmetry was non-significant but some other parameters showed significant laterality. However, none of the anatomic regions exhibited asymmetries of all studied metrics simultaneously. Cg, the fibre bundle whose FA laterality is often reported in DTI studies6, showed significant asymmetry of 6 parameters studied (FA, RD, AK, MK, KA, and RK), see Table 1. A between-group comparison (Fig.1) has shown significantly higher leftward laterality in Cg (on MK, p<0.010 and RK, p<0.017) and SLF (on MD, p<0.027 and AD, p<0.007) and rightward laterality in SLF_temp (on AD, p<0.016, MK, p<0.019 and RK, p<0.040).

Discussion

Generally, reports on hemispheric asymmetries are not consistent across the literature. Our results are generally in line with earlier DTI works reporting rightward FA asymmetry7 in CST and leftward FA asymmetry in Cg1 in Children but no FA asymmetry in Cg observed in adults8. We discuss additional information provided by diffusional kurtosis parameters in the context of a) microstructural features underlying asymmetry (such as those, leading to enhanced AK, RK, and MK in the left Cg) and b) subtle microstructural changes ongoing beyond late childhood.

Conclusion

We have shown that DKI parameters can provide more comprehensive information on brain asymmetry. The degree of asymmetry for some fibres was found to be much higher for children than for adults.

Abbreviations

ATR: Anterior thalamic radiation; CST: Corticospinal tract; Cg: Cingulum (cingulate gyrus); Ch: Cingulum (hippocampus); IFOF: Inferior fronto-occipital fasciculus; ILF: Inferior longitudinal fasciculus; SLF: Superior longitudinal fasciculus; UF: Uncinate fasciculus; SLF_temp: Superior longitudinal fasciculus in temporal part.

Acknowledgements

We thank Dr. Ivan I. Maximov, Dr. Heike Thönneßen and Laura Amort for their valuable contributions to acquisition and post-processing of DKI data in children and adults.

References

1. Takao, Hidemasa, et al., 2011. Human brain mapping 32.10: 1762-1773; 2. Hasan, Khader M., et al., 2010. Brain Structure and Function 214.4: 361-373; 3. Jensen, J.H., Helpern, J.A., et al., 2005. Magn. Reson. Med. 53, 1432-1440; 4. Wakana, Setsu, et al., 2005. MRI atlas of human white matter. Vol. 16. Amsterdam:: Elsevier; 5. Leemans, A., Jeurissen, B, et al., 2009. 17th Annual Meeting of Intl Soc Mag Reson Med. Vol. 209; 6. Gong, Gaolang, et al., 2005. Human brain mapping 24.2: 92-98; 7. Park, Hae-Jeong, et al., 2004. Neuroimage 23.1: 213-223; 8. Abe, Osamu, et al., 2002. Neurobiology of aging 23.3: 433-441.

Figures

Figure 1. The anatomic regions of significant AI change between children and adults are shown (red for mask and background for FA template from JHU).

Table 1. The AI of DKI parameters on CST and Cg for children and adults (* for p-value < 0.000694 after Bonferroni correction).



Proc. Intl. Soc. Mag. Reson. Med. 24 (2016)
3097