Synopsis

Brain maturations are thought to relate to behavioral acquisitions and cognitive development. Nevertheless, in vivo investigations of such relationships remain scarce in childhood. To bridge this gap, a multivariate index (D_M), which delineates the “maturational distance” between children and adults and leverages DTI-metrics complementarity, was utilized to characterize WM variation. We found that D_M showed significantly negative correlations with FSIQ in children aged 4-12 yr, especially in cingulum and superior longitudinal fasciculus. Besides, left hemispheric lateralization (higher correlations with FSIQ) was also observed. Our findings suggest D_M as a useful biomarker in detailing the brain WM maturation related to intelligence.

Introduction

Materials and Methods

The Institutional Review Broad of the first author’s affiliation approved this study and written informed consent were obtained from parents of all the children. Subjects Twenty-three children with no abnormality on MRI participated in the study. Intellectual ability of all children were in normal range (full scale IQ >85 scores). Intellectual ability was assessed by the Chinese Wechsler Young Children scale of Intelligence (C-WYCSI) for children aged 4 to 6 years and Chinese Wechsler Intelligence Scale for Children (C-WISC) children aged 6 to 18 years. MR Protocols All subjects were examined by using a 3.0T scanner (Signa HDxt, General Electric Medical System, Milwaukee, WI, USA) with an 8-channel head coil. Data acquisition included three-dimensional fast spoiled gradient-echo T1-weighted sequence (TR/TE, 10.2ms/4.6ms; NEX, 1; isotropic 1×1×1mm³; FOV, 24cm) and transverse fast spin-echo T2-weighted sequence (TR/TE, 4200ms/113ms; NEX of 1.5; matrix, 320×320; thickness, 4mm; FOV, 24cm), and DTI (18 directions; b value, 1000s/mm²; TR/TE, 11000ms/67.4ms; NEX of 1; thickness, 2.5mm; FOV, 24cm; matrix, 172×172). Data and statistical analysis DTI data were preprocessed by FMRIB software library (FSL; http://www.fmrib.ox.ac.uk/fsl) and Matlab software (MathWorks, Natick, Massachusetts). Four parameters of fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD) were calculated by using the above tools based on ROIs. Here we use the Johns Hopkins white matter tracts atlas as a reference. D_M was calculated by Mahalanobis distance according to Ref. 1. Considering the possible impacts of age, partial Pearson’s correlation analysis was used to explore the relationships between FSIQ and D_M, with age controlled.

All statistical analysis was performed by using SPSS 18.0 (SPSS, Chicago, IL, USA); p<0.05 was considered as statistically significant difference.

Results

Twenty-three children were included in this study (Table 1).

Results indicated that significant negative correlations between D_M and FSIQ scores were found in bilateral superior longitudinal fasciculus (SLF: rleft=0.506, P=0.016; rright=0.594, P=0.004), inferior fronto-occipital fasciculus (IFO: rleft=0.472, P=0.027; rright=0.425, P=0.048) and forceps_major (r=-0.450, P=0.036). In addition, strong correlation also existed in left cingulum (r=-0.731, P<0.001) and temporal part of SLF (r=-0.675, P=0.001) (Figure 1).

Discussion

In this study, a multivariate index D_M that leverages DTI-metrics complementarity was used to explore the associations between WM maturation and intelligence. In contrast to univariate metric, this index was more reliable as it takes into account complementary dependencies of DTI-metrics on different maturational processes, notably the decrease in water content and the myelination¹.

Previous study reported the close link between intelligence and anterior cingulate in children². Consistently, we also observed significant correlation of FSIQ with D_M in cingulum. Cingulum is thought to correlate with cognitive control in later life. Besides, more mature long-range tracts such as IFO, SLF and forceps_major have been demonstrated to show higher ability of working memory. These WM tracts were suggested as critical region in supporting the intelligence development^3,4. Similarly, we also found that a shorter D_M in these WM tracts corresponds to a higher level of FSIQ, further suggesting the importance of WM maturation in these regions related to intelligence.

In addition, left cingulum and temporal part of SLF presented remarkably higher correlations with FSIQ than right ones. This may suggest a left cerebral hemispheric lateralization in relation to intellectual ability. Although evidence of hemispheric specialization in left rostral cingulum, e.g. in the context of verbal or symbolic rules have been reported⁴, more details concerning these WM maturation with intelligence should also been explored.

Conclusions

Acknowledgements

References

1. Kulikova S, Hertz-Pannier L, Dehaene-Lambertz G, et al. Multi-parametric evaluation of the white matter maturation. Brain Structure and Function, 2015, 220(6): 3657-3672.
2. Casey B J, Trainor R, Giedd J, et al. The role of the anterior cingulate in automatic and controlled processes: a developmental neuroanatomical study. Dev Psychobiol, 1997, 30(1): 61-69.
3. Chiang M C, Barysheva M, Shattuck D W, et al. Genetics of brain fiber architecture and intellectual performance. J Neurosci, 2009, 29(7): 2212-2224.
4. Tamnes C K, Østby Y, Walhovd K B, et al. Intellectual abilities and white matter microstructure in development: a diffusion tensor imaging study. Human Brain Mapping, 2010, 31(10): 1609-1625.