Abstract

Little is known about the emergence of structural asymmetry of white matter tracts during early brain development. We examined whether and when asymmetry in diffusion parameters of limbic and association white matter pathways emerged in humans in 23 brains ranging from 15 gestational weeks (GW) up to 3 years of age (11 ex vivo and 12 in vivo cases) using high-angular resolution diffusion imaging tractography. Age-related development of laterality was not observed in a limbic connectional pathway (cingulum bundle or fornix). Among the studied cortico-cortical association pathways (inferior longitudinal fasciculus [ILF], inferior fronto-occipital fasciculus, and arcuate fasciculus), only the ILF showed development of age-related laterality emerging as early as the second trimester. Comparisons of ages older and younger than 40 GW revealed a leftward asymmetry in the cingulum bundle volume and a rightward asymmetry in apparent diffusion coefficient and leftward asymmetry in fractional anisotropy in the ILF in ages older than 40 GW. These results suggest that morphometric asymmetry in cortical areas precedes the emergence of white matter pathway asymmetry. Future correlative studies will investigate whether such asymmetry is anatomically/genetically driven or associated with functional stimulation.

Introduction

The 2 hemispheres of the human brain contribute asymmetrically to information processing. One of the most strikingly lateralized functions in the human is language, several aspects of which are predominantly processed in the left hemisphere in adults. In addition to recent studies suggesting that left–right differentiation of structure and function enhances the functional capacity of the brain (e.g., Halpern et al. 2005), clinically, developmental dyslexia (Galaburda et al. 1985; Galaburda 1993; Jenner et al. 1999), schizophrenia (Ribolsi et al. 2009; Oertel-Knochel and Linden 2011), and autism (Escalante-Mead et al. 2003; Herbert et al. 2005) are often linked to abnormal brain lateralization.

Asymmetries of brain surface morphometry emerge as early as the fetal stage (Hochestetter 1929; Fontes 1944; Chi et al. 1977; Heilbroner and Holloway 1988; Gilmore et al. 2007; Hill et al. 2010; Kasprian et al. 2011). Although a few groups have reported the overall development of white matter at fetal ages by using diffusion tractography (e.g., Kostovic and Vasung, 2009; Vasung et al. 2010; Takahashi et al. 2012), asymmetry in specific white matter tract pathways has only been reported in older ages, such as in children and adults (Lebel and Beaulieu 2009; Thiebaut de Schotten et al. 2011). For example, in children (>5 years old) and adults, the right inferior fronto-occipital fasciculus (IFOF), the left inferior longitudinal fasciculus (ILF) (Thiebaut de Schotten et al. 2011) and left arcuate fasciculus (AF) (Lebel and Beaulieu 2009) reveal early lateralized development, suggesting that asymmetry probably emerges in association pathways earlier than 5 years of age. In fact, significant neuroanatomic changes occur even earlier and during the first year of life (Provenzale et al. 2007). However, asymmetry of brain pathways in early development, as disclosed by imaging techniques, has not yet been directly addressed, because of several challenges related to MR imaging (e.g., motion in living fetuses in utero, the small size of the fetal brain, and less myelination, which requires very high spatial resolution for revealing detailed pathways) as well as the availability of fetal brains.

High-angular resolution diffusion imaging (HARDI) tractography enables identification of complex crossing tissue coherence in the brain (Tuch et al. 2003) even in immature brains (Takahashi et al. 2011, 2012), which are typically more challenging to segment due to a surplus of unmyelinated fibers. In this study, HARDI tractography was used to identify several white matter pathways, including limbic (cingulum bundle and fornix) and association (ILF, IFOF, and AF) pathways. Volume and diffusion properties [fractional anisotropy (FA) and apparent diffusion coefficient (ADC)] were obtained on the identified tracts to explore their emerging asymmetry. The aim of this study was to identify the temporal emergence of white matter pathways as detected by diffusion tractography and describe a spatio-temporal asymmetry between hemispheres in developing human brains starting as early as the second trimester. We hypothesized that, by term, asymmetry in white matter pathways thought to be linked to primary associative functions would be present earlier than pathways associated with higher cognitive functions.

Methods

Fetal Brain Specimens

Twenty-three brains were imaged for this study. Table 1 reports the age brackets, the number of specimens, and whether the brain was imaged ex vivo or in vivo. The ages were expressed as postgestational weeks (GW), which equal gestational age plus postnatal age. Seven postmortem brains were obtained from the Department of Pathology, Brigham and Women's Hospital (BWH; Boston, MA, USA), and 4 postmortem brains (19, 21, and 22 GW, and 8M) were provided by the Allen Institute Brain Bank (AIBB; Seattle, WA, USA). Postmortem specimens were obtained with full parental consent. The primary cause of death was complications of prematurity. The brains were grossly normal, and standard autopsy examinations of all brains undergoing postmortem HARDI revealed minimal or no pathologic abnormalities at the macroscopic level. Ex vivo fetal and postnatal brains and living postnatal infant brains were included in the study, which allowed us to reveal brain structure in unforeseen detail. Although ex vivo imaging is superior, we have previously shown that ex vivo and in vivo imaging produce comparable tracking results at fetal, neonatal, and toddler ages (Xu et al. 2014).

Table 1

Characteristics of brains

Subject Source Age Brain image 
BWH 15 GW Ex vivo 
BWH 17 GW Ex vivo 
AIBB 19 GW Ex vivo 
BWH 20 GW Ex vivo 
BWH 20 GW Ex vivo 
BWH 20 GW Ex vivo 
BWH 21 GW Ex vivo 
BWH 21 GW Ex vivo 
AIBB 21 GW Ex vivo 
10 AIBB 22 GW Ex vivo 
11 BCH 30 GW In vivo 
12 BCH 30 GW In vivo 
13 BCH 34 GW In vivo 
14 BCH 40 GW In vivo 
15 BCH 40 GW In vivo 
16 BCH 40 GW In vivo 
17 BCH 40 GW In vivo 
18 AIBB 8 monthsa Ex vivo 
19 BCH 20 months a In vivo 
20 BCH 2 yearsa In vivo 
21 BCH 3 yearsa In vivo 
22 BCH 3 yearsa In vivo 
23 BCH 3 yearsa In vivo 
Subject Source Age Brain image 
BWH 15 GW Ex vivo 
BWH 17 GW Ex vivo 
AIBB 19 GW Ex vivo 
BWH 20 GW Ex vivo 
BWH 20 GW Ex vivo 
BWH 20 GW Ex vivo 
BWH 21 GW Ex vivo 
BWH 21 GW Ex vivo 
AIBB 21 GW Ex vivo 
10 AIBB 22 GW Ex vivo 
11 BCH 30 GW In vivo 
12 BCH 30 GW In vivo 
13 BCH 34 GW In vivo 
14 BCH 40 GW In vivo 
15 BCH 40 GW In vivo 
16 BCH 40 GW In vivo 
17 BCH 40 GW In vivo 
18 AIBB 8 monthsa Ex vivo 
19 BCH 20 months a In vivo 
20 BCH 2 yearsa In vivo 
21 BCH 3 yearsa In vivo 
22 BCH 3 yearsa In vivo 
23 BCH 3 yearsa In vivo 

GW, gestational week; BWH, Brigham and Women's Hospital; AIBB, Allen Institute Brain Bank; BCH, Boston Children's Hospital.

aFor analyses, all ages were scaled to weeks by adding 40 weeks. For instance, 8 months as 72 weeks, 20 months as 120 weeks, 2 years as 144 weeks, and 3 years as 196 weeks.

All living participants (12 newborns and children) had clinically indicated brain MRI studies that were diagnostically interpreted to show no abnormalities. Indications for imaging included concern for hypoxic ischemic injury, apnea, and transient choreiform movements after an upper respiratory tract infection. None had clinical concerns for a congenital malformation or genetic disorder. All research protocols were approved by the institutional review board.

Tissue Preparation for HARDI

At the time of autopsy, all brains were immersion fixed. The brains from BWH were stored in 4% paraformaldehyde, and the brains from AIBB were stored in 4% periodate-lysine-paraformaldehyde (PLP). During MR image acquisition, BWH brains were placed in Fomblin solution (Ausimont, Thorofare, NJ, USA) (e.g., Takahashi et al. 2012) and AIBB brains were placed in 4% PLP. While these different solutions tend to change the background contrast (i.e., we see a dark background outside of the brain using Fomblin, and a bright background using PLP), these solutions do not specifically change diffusion properties (e.g., FA and ADC) within the brain parenchyma.

Diffusion MRI Procedures

Different scanner systems were used to accommodate different brain sizes. Magnetic resonance (MR) coils that best fit each brain sample were used to ensure optimal imaging. The postmortem brain specimens from BWH were imaged with a 4.7 T Bruker Biospec MR system (specimens from 15 to 21 GW), and specimens from the AIBB were imaged with a 3 T Siemens MR system (3 fetal [19, 21, 22 GW] and 8M specimens) at the A. A. Martinos Center, Massachusetts General Hospital, Boston, MA, USA. The 3 T system was used to accommodate the AIBB brains that were in cranio and did not fit in the 4.7 T bore. To improve the imaging quality and obtain the best signal-to-noise ratio and high spatial resolution, we used custom-made MR coils with one channel on the 4.7 and 3 T systems (Takahashi et al. 2012; Kolasinski et al. 2013; Xu et al. 2014).

For the BWH brains, a 3D diffusion-weighted spin-echo echo-planar imaging (SE-EPI) sequence was used with a repetition time/echo time (TR/TE) of 1000/40 ms, with an imaging matrix of 112 × 112 × 112 pixels. Sixty diffusion-weighted measurements (with the strength of the diffusion weighting, b = 8000 s/mm2) and one nondiffusion-weighted measurement (no diffusion weighting or b = 0 s/mm2) were acquired with δ = 12.0 ms and Δ = 24.2 ms. The spatial resolution was 440 × 500 × 500 µm. For the brains from the AIBB, diffusion-weighted data were acquired over 2 averages using a steady-state free-precession sequence with TR/TE = 24.82/18.76 ms, α = 60°, and the spatial resolution was 400 × 400 × 400 μm for brains at 19, 21, and 22 GW and 900 × 900 × 900 μm for the 8M brain. Diffusion weighting was isotropically distributed along 44 directions (b = 730 s/mm2) with 4 b = 0 images. We determined the highest spatial resolution for each brain specimen with an acceptable signal-to-noise ratio of more than 130.

The brains of living patients were imaged on a 3 T Siemens MR system, Boston Children's Hospital, Boston, MA, USA. The diffusion pulse sequence used for imaging live participants was a diffusion-weighted SE-EPI sequence, TR/TE 8320/88 ms, with an imaging matrix of 128 × 128 × 64 pixels. The spatial resolution was 2 × 2 × 2 mm. Thirty diffusion-weighted measurements (b = 1000 s/mm2) and 5 nondiffusion-weighted measurements (b = 0 s/mm2) were acquired with δ = 40 ms and Δ = 68 ms.

Reconstruction and Identification of Tractography Pathways

White matter tracts were reconstructed in each brain with the Diffusion Toolkit and TrackVis programs (http://trackvis.org). A streamline algorithm for diffusion tractography was used (Mori et al. 1999), as in previous publications (Schmahmann et al. 2007; D'Arceuil et al. 2008; Takahashi et al. 2010, 2011, 2012). The term “streamline” refers to connecting tractography pathways using a local maximum or maxima. This method is true for both DTI and HARDI. The streamline technique is limited in its ability to resolve crossing pathways when used with the traditional DTI technique, because one simply connects the direction of the principal eigen vector on a tensor to produce the DTI tractography pathways. This feature is a recognized limitation of DTI (Mori et al. 1999). Hence, in the current study, we used HARDI, which detects multiple local maxima on an orientation distribution function (ODF). Using each local maxima on an ODF, we applied the streamline algorithm to initiate and continue tractography (Tuch et al. 2003) using all the local maxima of an ODF to produce HARDI tractography pathways. This method enabled us to identify crossing pathways within a voxel.

Trajectories were propagated by consistently pursuing the orientation vector of least curvature. Tracking was terminated when the angle between two consecutive orientation vectors was greater than the given threshold (40°) or when the fibers extended outside of the brain surface. For the latter determination, mask (boundary) images of the brains created by MRIcro (www.sph.sc.edu/comd/rorden/mricro.html) were used in order to determine coherence in the brain itself and not in the surrounding immersion fluid. Of note, the mask image is a 3D binary image created from a mean diffusion image of each brain assessment with endpoints constrained to the brain tissue itself. The brain mask volumes were used to terminate tractography structures instead of the FA threshold (Schmahmann et al. 2007; Wedeen et al. 2008; Takahashi et al. 2010, 2012; Vishwas et al. 2010), because progressive myelination and crossing fibers in the developing brain can result in low FA values that may potentially incorrectly terminate tractography tracing in the gray matter.

The following 5 association pathways were segmented: the cingulum bundle, fornix, ILF, IFOF, and the arcuate fasciculus (AF). Briefly, following previously described methods (Catani and Thiebaut de Schotten 2008), a two-ROI approach was used to segment the ILF, inferior IFOF, AF, cingulum, and fornix. To identify tractography pathways for the ILF, we placed an ROI in the occipital and anterior temporal regions and restricted tracking to pathways running only between these 2 ROIs. This method identifies the ILF from the uncinate as well as other pathways. For visualization purposes in Figure 1, the number of detected tractography connections were restricted by 75% of the connections that touched an axial slice (Fig. 1, left) and a sagittal slice (Fig. 1, right) with a thickness of 10 voxels. Tractography pathways were then restricted to start and end within the plane. Of note, the emergence of white matter tracts will be written to mean the detection of fibers by diffusion tractography.

Figure 1.

Maximum projection images of whole brains at different ages. Axial and sagittal images of whole brains at selective references ages (17 GW to 2 years) are shown. Note the differences in scale of the brains, indicating a rapid growth in both size and shape.

Figure 1.

Maximum projection images of whole brains at different ages. Axial and sagittal images of whole brains at selective references ages (17 GW to 2 years) are shown. Note the differences in scale of the brains, indicating a rapid growth in both size and shape.

The color-coding of tractography pathways in Figures 1 and 2 was based on a standard red-green-blue code that was applied to the vector in each brain area to show the direction of the tractography pathways (green for right-left, red for dorsal-ventral, and blue for anterior-posterior, the latter in pathways that are confined to the coronal slice analyzed).

Figure 2.

Development of white matter tract pathways. Tractography at several reference ages of the fornix, cingulum bundle, ILF, and IFOF are shown for the left and right hemispheres. As a reference, the right-most column illustrates the white matter tract against brain background images in sagittal, coronal, and axial slices. The left-most column illustrates a reference brain for orientation. Between 21 and 30 GW, the caliber of the fornix markedly diminished in thickness. The left cingulum bundle extended tractography fibers anteriorly earlier on the left than on the right (yellow arrowheads; 21 GW). However, by 30 GW, anterior and posterior tractography fibers were seen arising from both the left and right cingulum bundles. At 40 GW, the right cingulum bundle showed increased branching fibers both anteriorly and posteriorly compared with the left. By 20 months, fibers extending to the infero-medial frontal regions (light blue arrowheads; 20 months, 2 years, 3 years) and parahippocampal regions were observed (dark blue arrowheads; 20 months, 2 years, 3 years). Both the ILF and IFOF became longer during development. Both pathways showed a fanned appearance posteriorly during similar ages of development (right ILF and IFOF, 34 GW; left ILF and IFOF, 20 months), which changed to a more elongated appearance by 3 years (see text for further details).

Figure 2.

Development of white matter tract pathways. Tractography at several reference ages of the fornix, cingulum bundle, ILF, and IFOF are shown for the left and right hemispheres. As a reference, the right-most column illustrates the white matter tract against brain background images in sagittal, coronal, and axial slices. The left-most column illustrates a reference brain for orientation. Between 21 and 30 GW, the caliber of the fornix markedly diminished in thickness. The left cingulum bundle extended tractography fibers anteriorly earlier on the left than on the right (yellow arrowheads; 21 GW). However, by 30 GW, anterior and posterior tractography fibers were seen arising from both the left and right cingulum bundles. At 40 GW, the right cingulum bundle showed increased branching fibers both anteriorly and posteriorly compared with the left. By 20 months, fibers extending to the infero-medial frontal regions (light blue arrowheads; 20 months, 2 years, 3 years) and parahippocampal regions were observed (dark blue arrowheads; 20 months, 2 years, 3 years). Both the ILF and IFOF became longer during development. Both pathways showed a fanned appearance posteriorly during similar ages of development (right ILF and IFOF, 34 GW; left ILF and IFOF, 20 months), which changed to a more elongated appearance by 3 years (see text for further details).

Statistical Analysis

The measures of FA, ADC, and volume are automatically derived by TrackVis for each segmented pathway after FA and ADC maps of the whole brain are loaded. FA, ADC, and volume outcomes of each segmented pathway were obtained. The volume of pathways was calculated by counting the number of voxels that touched or passed through by the pathways.

The laterality index (LI) for FA (FALI), ADC (ADCLI), and volume (VolLI) of each white matter tract was compared at different developmental time points. LIs were calculated as follows: LI = (L − R)/{0.5 × (L + R)}. LIs range from −2 to 2, and positive and negative LI values correspond to left- and rightward asymmetry, respectively (Galaburda et al. 1985; Caviness et al. 1996; Thiebaut de Schotten et al. 2011).

All statistical analyses were performed with SPSS version 19.0 (IBM SPSS, Chicago, IL, USA). To identify trends at each developmental time point, scatterplots were created for the continuous variables. Postmortem fixation processes may affect FA and ADC values and confound results. To account for such confounders, partial correlations with in vivo and ex vivo as the control variable were performed to facilitate comparisons across in vivo and ex vivo imaged brains. Additionally, subgroup correlation analyses of in vivo and ex vivo imaged brains were performed with Spearman's rank correlation. Box–whisker plots were generated to visualize the distributions, and the Mann–Whitney U-test was performed to compare the LIs by age. Statistical significance was set at P < 0.05.

Results

Qualitative Description of the Development of White Matter Pathways

The fornix was thickest at 21 GW and gradually thinned with age. The cingulum bundle was smooth and thin until 21 GW and revealed very short branches at both the anterior and posterior ends (Fig. 2, yellow arrowheads at 15 GW). The branching ends extended longitudinally with age and became more conspicuous at later ages (yellow arrowheads). The main body of the cingulum bundle did not extend into the ventral brain regions before 40 GW, but components of the bundle were observed extending to the medial, inferior frontal, and parahippocampal regions at 40 GW and later (Fig. 2, blue arrowheads).

The ILF was dense at 21 and 30 GW. Both the ILF and IFOF grew in length during development. The AF became evident by tractography by 30 GW. Until 34 GW, the AF was sparse, short and did not have obvious branches. At 40 GW, however, short branches arose from the main body of the tract (Fig. 3, red arrowheads; arrowheads are not shown in later ages to allow better visualization), and they became longer with age. The main body of the AF grew increasingly thicker by 3 years.

Figure 3.

Development of the arcuate fasciculus. Tractography at several reference ages for the arcuate fasciculus (AF) are shown for each hemisphere. As a reference, the right-most column illustrates the white matter tract against a brain background image in sagittal, coronal, and axial slices. The left-most column illustrates a reference brain for orientation. The AF became evident by tractography by 30 GW. At 40 GW, short branching pathways from the main body of the tract were identified (red arrowheads), which gradually became longer at later developmental ages (see text for further details).

Figure 3.

Development of the arcuate fasciculus. Tractography at several reference ages for the arcuate fasciculus (AF) are shown for each hemisphere. As a reference, the right-most column illustrates the white matter tract against a brain background image in sagittal, coronal, and axial slices. The left-most column illustrates a reference brain for orientation. The AF became evident by tractography by 30 GW. At 40 GW, short branching pathways from the main body of the tract were identified (red arrowheads), which gradually became longer at later developmental ages (see text for further details).

Overall, the thickness, density, and branching patterns of each pathway revealed subtle differences between the left and right sides.

Correlations Between Laterality Indices and Age

Correlations between age and laterality indices (LIs) for FA, ADC, and volume for each white matter tract are reported in Table 2 (see also associated scatterplots shown in Fig. 4). The LIs for each outcome weakly correlated with age in the cingulum bundle, fornix, IFOF, and AF (r < 0.30; Table 2). The cingulum bundle showed a marginally significant age-related leftward asymmetry in volume (r = 0.44, P = 0.07). Also, a significant age-related leftward asymmetry in FA for the ILF was seen during this period of development (r = 0.53, P = 0.03).

Table 2

Correlations of laterality indices by agea

Structure Laterality index
 
LIVolume versus age LIFA versus age LIADC versus age 
Cingulum bundle 
 All data (n = 19) 0.44 0.02 0.02 
 In vivo (n = 11) 0.38 0.01 −0.06 
 Ex vivo (n = 8) 0.70 0.30 −0.05 
Fornix 
 All data (n = 20) 0.01 0.03 −0.01 
 In vivo (n = 12) 0.22 −0.03 −0.35 
 Ex vivo (n = 8) −0.22 0.00 −0.18 
Inferior longitudinal fasciculus 
 All data (n = 18) −0.13 0.53* −0.46 
 In vivo (n = 12) −0.28 0.63* −0.61* 
 Ex vivo (n = 7) 0.93* 0.13 −0.10 
Inferior fronto-occipital fasciculusb 
 All data (n = 15) 0.02 −0.05 −0.01 
 In vivo (n = 12) 0.29 −0.16 −0.21 
Arcuate fasciculusc 
 In vivo (n = 12) −0.24 0.32 −0.10 
Structure Laterality index
 
LIVolume versus age LIFA versus age LIADC versus age 
Cingulum bundle 
 All data (n = 19) 0.44 0.02 0.02 
 In vivo (n = 11) 0.38 0.01 −0.06 
 Ex vivo (n = 8) 0.70 0.30 −0.05 
Fornix 
 All data (n = 20) 0.01 0.03 −0.01 
 In vivo (n = 12) 0.22 −0.03 −0.35 
 Ex vivo (n = 8) −0.22 0.00 −0.18 
Inferior longitudinal fasciculus 
 All data (n = 18) −0.13 0.53* −0.46 
 In vivo (n = 12) −0.28 0.63* −0.61* 
 Ex vivo (n = 7) 0.93* 0.13 −0.10 
Inferior fronto-occipital fasciculusb 
 All data (n = 15) 0.02 −0.05 −0.01 
 In vivo (n = 12) 0.29 −0.16 −0.21 
Arcuate fasciculusc 
 In vivo (n = 12) −0.24 0.32 −0.10 

aPartial correlations with in vivo and ex vivo as a controlled variable were used for analyses including structures from both in vivo and ex vivo brains. Spearman correlations were used for subgroup analyses (in vivo and ex vivo separately).

bInferior fronto-occipital fasciculi were present in n = 3 ex vivo brains.

cArcuate fasciculus structures were segmented from in vivo scanned brains; none of the available ex vivo brain specimens revealed identifiable arcuate fasciculi.

*P ≤ 0.05.

Figure 4.

Scatterplots of laterality indices versus age. Scatterplots were generated for each white matter tract to visually examine age-related correlations between age (x-axis) versus the laterality index for FA, ADC, and volume (y-axis). A red dotted line was drawn at 40 GW. Datapoints comprising fetal ages <40 GW appeared scattered above and below the 0-line (horizontal black line; no asymmetry), likely representing interindividual variability. However, after 40 GW, most datapoints clustered above, below or at the 0-line. Open circles represent in vivo imaged brains, and solid diamonds represent ex vivo imaged brains.

Figure 4.

Scatterplots of laterality indices versus age. Scatterplots were generated for each white matter tract to visually examine age-related correlations between age (x-axis) versus the laterality index for FA, ADC, and volume (y-axis). A red dotted line was drawn at 40 GW. Datapoints comprising fetal ages <40 GW appeared scattered above and below the 0-line (horizontal black line; no asymmetry), likely representing interindividual variability. However, after 40 GW, most datapoints clustered above, below or at the 0-line. Open circles represent in vivo imaged brains, and solid diamonds represent ex vivo imaged brains.

Subgroup analyses with in vivo and ex vivo imaged brains were also performed. This analysis showed significant results for the ILF. The ILF in in vivo imaged brains (n = 12; 30 GW to 3 years) showed a significant age-related leftward asymmetry with FA (r = 0.63, P = 0.029). Conversely, there was a significant age-related rightward asymmetry with ADC in the ILF pathways in in vivo imaged brains (r = −0.61, P = 0.034). In ex vivo imaged brains, there was a significant age-related leftward asymmetry in the volume of the ILF pathways (n = 8; 20 GW to 22 GW; r = 0.93, P = 0.001).

Comparisons of Laterality Indices Before and After 40 GW

Gross visual inspection of the scatterplots for volume, FA, and ADC LIs by age suggested a clustering of data points after 40 GW (Fig. 4). To further investigate this pattern, subgroup analyses by age were performed with in vivo imaged brains (Fig. 5), because this subgroup included brains both younger and older than 40 GW. Additionally, by only using in vivo data, confounding effects due to MRI scan conditions and fixation artifacts are reduced. The AF was not included in this analysis, because the former disappeared by 40 GW and only in vivo data were available for the AF. The ILF pathways showed significant leftward asymmetry in FA (P = 0.007) and rightward asymmetry in ADC (P = 0.015) in brains aged >40 GW when compared with brains aged ≤40 GW (Fig. 5). Additionally, the cingulum bundle showed significant leftward asymmetry in volume in brains aged >40 GW when compared with brains aged ≤40 GW (P = 0.045).

Figure 5.

Comparison of laterality indices before and after 40 GW (using only in vivo data). Box–whisker plots were generated to compare the medians and distributions of the laterality index for each outcome by age. The cingulum bundle in brains aged >40 GW showed a significant leftward asymmetry in volume compared with brains aged <40 GW. The ILF showed a significant leftward asymmetry in FA and rightward asymmetry in ADC in brains aged >40 GW compared with brains aged <40 GW. The circles represent outliers and the asterisks represent extreme values at the respectively labeled ages in GW adjacent to the circle or asterisk {values greater than [2 × (1.5 × interquartile range)]}.

Figure 5.

Comparison of laterality indices before and after 40 GW (using only in vivo data). Box–whisker plots were generated to compare the medians and distributions of the laterality index for each outcome by age. The cingulum bundle in brains aged >40 GW showed a significant leftward asymmetry in volume compared with brains aged <40 GW. The ILF showed a significant leftward asymmetry in FA and rightward asymmetry in ADC in brains aged >40 GW compared with brains aged <40 GW. The circles represent outliers and the asterisks represent extreme values at the respectively labeled ages in GW adjacent to the circle or asterisk {values greater than [2 × (1.5 × interquartile range)]}.

Discussion

Using diffusion tractography, we investigated the temporal emergence of white matter tracts and their developing asymmetry in human brains from 15 GW to 3 years. Significant age-related leftward hemispheric asymmetry in the ILF pathway in FA emerged as early as 15 GW. In contrast, asymmetry of the white matter pathways associated with higher order cognitive functions, such as the AF, was not observed up to 3 years of age. These results suggest that although emerging asymmetry of white matter pathways is prenatally present, more robust asymmetry likely emerges postnatally.

Limbic Pathways

Age-related correlations with FALI, ADCLI, and VolLI were not observed in the fornix and cingulum bundle (Table 2). The result in the fornix is consistent with the literature (Thiebaut de Schotten et al. 2011). Interestingly, although several studies report leftward asymmetry in FA of adult cingulum bundles (Gong et al. 2005; Takao et al. 2011; Yin et al. 2013), we did not see a significant age-related correlation for the cingulum bundle. Developing regional asymmetry among the different segments of the cingulum bundle may explain why there was no significant asymmetry in FA. Gong et al. (2005) segmented the cingulum bundle in adult humans into multiple parts and reported leftward asymmetry in the anterior segments. Given the general posterior–anterior direction of white matter development, perhaps during the early developmental ages, posterior segments of the cingulum bundle mature earlier.

Association Pathways

We hypothesized that asymmetry in white matter pathways thought to be linked to primary associative functions would be present earlier than in pathways associated with higher cognitive functions. A previous research study proved that HARDI tractography can reliably resolve crossing white matter pathways along the ILF (Berman et al. 2013). Interestingly, although both the ILF and IFOF pathways are associated with visual processes (Chechlacz et al. 2012), our results showed asymmetry in the ILF but not the IFOF during the studied ages. The ILF, which extends from the occipital to the temporal lobes (Schmahmann and Pandya 2009), is thought to be associated with object recognition. Indeed, children with an object recognition deficit revealed a significantly decreased mean-FA of the ILF in the left hemisphere with resultant loss of asymmetry of the ILF pathway (Ortibus et al. 2012). Compatible with the results of Ortibus et al., our study revealed a significant age-related leftward asymmetry in FA of the ILF pathway.

In contrast, the IFOF revealed no significant age-related asymmetry, which suggests that asymmetry in tracts associated with object recognition likely precedes asymmetry in tracts involved in a more integrative role. In fact, the IFOF is a cortico-cortical pathway extending from the occipital to inferior frontal lobes and is thought to play an integrative role in visual processing, such as emotion recognition through facial expressions (Philippi et al. 2009). In the adult brain, laterality in both the ILF and IFOF has been described (Thiebaut de Schotten et al. 2011; Kucyi et al. 2012), indicating that asymmetry eventually develops. Studies suggest that cortical maturation is directional with the inferior temporal and inferior frontal lobes being the last regions to mature (Gogtay et al. 2004; Takahashi et al. 2012). Hence, given the ages studied, asymmetry in the IFOF, a ventral pathway, may emerge outside the ages analyzed in this study.

The results of the ILF and IFOF pathways raise an interesting developmental difference in white matter pathway and sulcal asymmetry. Robust sulcal asymmetry is described in the temporal lobes starting in the late second trimester (Rajagopalan et al. 2011). This contrasts with the degree of white matter pathway asymmetry in the same region at the same developmental stage. One explanation is that early brain growth may be driven primarily by gray matter rather than white matter. During the first year of life, gray matter accounting for hemispheric growth was reported to increase by 149% contrasting with an 11% increase in white matter volume (Knickmeyer et al. 2008). The changes seen in sulcation of the cortical gray matter may thus be driven by the dramatic changes in gray matter volume, whereas white matter tract asymmetry may be related to functional stimulation.

Arcuate Fasciculus

Despite many studies indicating a leftward laterality in the adult AF (Buchel et al. 2004; Takao et al. 2011), no asymmetry was observed in the AF up to 3 years of age. This finding may be explained by the developmental direction of white matter maturation. Thiebaut de Schotten and colleagues (2011) segmented the adult AF into posterior, anterior, and long segments and found asymmetry in the anterior and long segments but no asymmetry in the posterior segment. Thus, with the general posterior–anterior direction of white matter development, the posterior segment may mature earlier and have a dominating effect in overall volume, FA, and ADC laterality.

Furthermore, studies suggest asymmetry in AF connectivity develops contemporaneously with language and reading acquisition. Literacy is thought to reinforce left temporo-parietal connections and the microstructure of the AF, as seen by one study comparing the AF of literates and illiterates (Dehaene et al. 2010; Thiebaut de Schotten et al. 2011). With reading acquisition, the functional activation of the planum temporale, an important source of axons to the AF, was seen to be dramatically asymmetric in favor of the left side (Dehaene-Lambertz et al. 2006). Furthermore, it is not until age 3–4 years that children begin to use sentences with more than 4 words to meet their language-related developmental milestones (Simms and Schum 2011). Hence, robust asymmetry in the AF probably emerges after 3 years of age.

Limitations of the Current Study

There are several limitations in this study. First, both ex vivo and in vivo data were combined in some of the analyses, which may result in variations of postmortem conditions, specifically in ex vivo MR imaging. However, ex vivo imaging, especially of fetal brains, provides high-resolution tractography, which enables greater understanding of fetal brain connectivity. Obtaining the same degree of resolution from in utero imaging remains challenging. Nevertheless, we acknowledge this limitation and control for differences between the 2 groups by statistical methods, such as partial correlation analyses. We also perform subgroup analyses with only in vivo brains to draw conclusions about emerging laterality (Fig. 5).

Second, the number of brains at each age is few, and the study is limited by brains aged up to 3 years. Future studies will aim to have a larger sample size with brains at a wider age interval to further investigate the development of asymmetry in white matter tract pathways. With older ages, additional functional information can be collected. For instance, insight on the effects of language acquisition on structural white matter tract asymmetry during development could shed light on when is the most vulnerable periods during brain development. Additionally, hand dominance is also known to be strongly lateralized in the human brain. The literature regarding handedness and brain asymmetry remains conflicting. Whether hand dominance influences white matter tract asymmetry at these early developmental stages also remains unclear. Future studies will aim to take a systematic approach to investigating the development of asymmetry by age, while taking into account other factors, such as gender, language acquisition, and hand dominance.

Future Directions

Understanding fetal and neonatal brain connectivity is a challenging but common goal in developmental neurobiology. Challenges are related to the dynamic changes that occur throughout development as well as the presence of exuberant axons at these early ages. Many lines of evidence show that the developing neural network is first established by a surplus of connections that subsequently undergo a refinement process of developmental axon pruning (Huttenlocher 1979; Innocenti et al. 1986; LaMantia and Rakic 1990; Huttenlocher and Dabholkar 1997). Compatible with the literature, our results also show that white matter pathways involved in higher-order cognitive or integrative functions, such as the AF and IFOF, are initially symmetric. Developmental axon pruning mechanisms likely follow and refine axonal connections mediating the development of asymmetry. Glial lysosomal activity has previously been shown to be a central feature of developmental axon pruning (Song et al. 2008), and in the future, contrast agents with the ability to mark increased areas of glial lysosomal activity may be a potential means for highlighting regions of developmental axon pruning that lead to structural asymmetry.

This study focused on the development of white matter tracts that exist completely in their respective hemispheres. Commissural fibers, such as the corpus callosum and the anterior and posterior commissures, were not included in this study as they are midline structures. The growth of the corpus callosum was observed from 4 GW (Rakic and Yakovlev 1968), and the maturation of the commissure pathways has been reported to be more prolonged than the association fibers with development continuing well into the mid-20s (Lebel et al. 2008). It has, however, been shown that similar developmental phenomena occur in the corpus callosum in rhesus monkeys, whereby exuberant axons are selectively eliminated, generating asymmetry if an unequal number of axons are pruned on each side (LaMantia and Rakic 1990). Hence, although for anatomic and temporal reasons, we felt including these fibers were out of the scope of this study, future studies will further investigate the asymmetry of these midline structures by tractography.

Whether functional asymmetry precedes structural refinement still remains unclear. Correlating functional MRI and tractography with probabilistic estimations (e.g., Takahashi et al. 2007, 2013; Berman et al. 2013) to elucidate a temporal relationship between structural and functional laterality is also a future aim. Moreover, the role of gender in white matter tract asymmetry will be examined with the recruitment of a larger sample size and wider range of ages. Establishing a functional and structural map for normal development of asymmetry will provide additional insight about cognitive development in children and identify the most vulnerable periods of white matter pathway development, which may affect children with learning disabilities.

Notes

Conflict of Interest: None declared.

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