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J Neurosci Res. Author manuscript; available in PMC 2013 Feb 1.
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PMCID: PMC3237749

Regional Brain Axial and Radial Diffusivity Changes During Development


The developing human brain shows rapid myelination and axonal changes during childhood, adolescence and early adulthood, requiring successive evaluations to determine normative values for potential pathological assessment. Fiber characteristics can be examined by axial and radial diffusivity procedures, which measure water diffusion parallel and perpendicular to axons, and primarily show axonal status and myelin changes, respectively. Such measures are lacking from wide-spread sites for the developing brain. Diffusion tensor imaging data were acquired from 30 healthy subjects (age, 17.7±4.6, range 8–24 years; body-mass-index, 21.5±4.5 kg/m2; 18 male) using a 3.0-Tesla MRI scanner. Diffusion tensors were calculated, principal eigenvalues determined, and axial and radial diffusivity maps calculated and normalized to a common space. A set of regions-of-interest were outlined from wide-spread brain areas within rostral, thalamic, hypothalamic, cerebellar, and pontine regions, and average diffusivity values were calculated using normalized diffusivity maps and these regions-of-interest masks. Age-related changes were assessed with Pearson’s correlations, and gender differences evaluated with Student’s t-tests. Axial and radial diffusivity values declined with age in the majority of brain areas, except for mid hippocampus, where axial diffusivity values correlated positively with age. Gender differences emerged within putamen, thalamic, hypothalamic, cerebellar, limbic, temporal, and other cortical sites. Documentation of normal axial and radial diffusivity values will help assess disease-related tissue changes. Axial and radial diffusivity change with age, with fiber structure and organization differing between sexes in several brain areas. The findings may underlie gender-based functional characteristics, and mandate partitioning age- and gender-related changes during developmental brain pathology evaluation.

Keywords: Diffusion tensor imaging, Axons, Myelin, Brain maturation, Water diffusion


The developing human brain shows rapid changes in axonal characteristics with age, including axonal density, caliber, and myelin (Barnea-Goraly et al. 2005; Mukherjee et al. 2002). Disease-related pathological processes induce alterations in axonal integrity, including axonal and myelin loss (DeLuca et al. 2006; Kumar et al. 2008; Kumar et al. 2010; Mitchell et al. 2003; Trapp et al. 1998). Pathological axonal changes can be evaluated effectively after controlling for age-related variations in axonal characteristics. However, description of normal age-related axonal characteristics from wide-spread sites is lacking for the developing brain.

Such normative age-related tissue changes can be determined by axial and radial diffusivity procedures. Axial diffusivity measures diffusion of water parallel to axons, and primarily shows axonal status, and radial diffusivity measures water diffusion perpendicular to fibers, and mainly indicates myelin changes (Song et al. 2002; Song et al. 2005). Axial diffusivity values increase in areas with reduced axonal density or caliber, and radial diffusivity values increase with loss of myelin integrity in chronic pathological conditions (Della Nave et al. 2010; Hasan et al. 2008; Kumar et al. 2008; Kumar et al. 2010). However, axial and radial diffusivity values decrease with increased corresponding axonal density or caliber, and myelin increases with age in the developing brain (Mukherjee et al. 2002; Qiu et al. 2008; Suzuki et al. 2003). Thus, axial and radial diffusivity measures can help determine changes in fiber characteristics with age in healthy subjects during childhood, adolescence and young adulthood. Since females differ significantly in cerebral blood flow over males (Jones et al. 1998), and blood perfusion characteristics may significantly alter the course of pathological or progressive changes, any evaluation of axonal characteristics must include the contribution of gender.

Our aim was to assess normal age-related changes in axial and radial diffusivity in wide-spread brain areas, and determine sex-related differences in those brain sites in healthy subjects from childhood, adolescents and young adults.

Materials and Methods


We studied 30 healthy subjects (Male, 18, mean age ± SD = 18.5±4.6 years, age range = 10–24 years, mean body-mass-index ±SD = 22.2±4.9 kg/m2; Female, 12, mean age ± SD = 16.5±4.6 years, age range = 8–22 years, mean body-mass-index ±SD = 20.5±3.7 kg/m2). All subjects had participated in previously-published pediatric studies of issues unrelated to the current interest (Kumar et al. 2010; Ogren et al. 2010); thus, male and female subjects were not age-matched, but were of the same age-range. Subjects were recruited through advertisements at the university campus and from the neighboring community. We excluded all subjects with conditions that might affect brain tissue, brain images, or might not be safe for subjects in a high-magnetic field environment, as outlined in the web-site of the Institute for Magnetic Resonance Safety, Education, and Research (http://www.mrisafety.com/).

All subjects and their parents/guardians gave informed written consent/assent prior to the study, and the study protocol was approved by the Institutional Review Board of the University of California at Los Angeles. Personal identifiable information was removed from the records after completion of the analyses.

Magnetic resonance imaging

We performed all brain imaging studies using a 3.0-Tesla magnetic resonance imaging (MRI) scanner (Magnetom Tim-Trio; Siemens, Erlangen, Germany). We used a receive-only 8-channel phased-array head-coil and a whole-body transmitter coil for MRI studies. Foam pads on both sides of the head were used to reduce head motion during MRI. High-resolution T1-weighted images were collected using a magnetization prepared rapid acquisition gradient-echo (MPRAGE) pulse sequence [repetition-time (TR) = 2200 ms; echo-time (TE) = 2.34 ms; inversion time = 900 ms; flip angle (FA) = 9°; matrix size = 320×320; field-of-view (FOV) = 230×230 mm; slice thickness = 0.9 mm; slices = 192]. Proton-density (PD) and T2-weighted images were acquired, covering the whole brain, using a dual-echo turbo spin-echo pulse sequence (TR = 10,000 ms; TE1, 2 = 12, 119 ms; FA = 130°; matrix size = 256×256; FOV = 230×230 mm; slice thickness = 3.5 mm; turbo factor = 5). Diffusion tensor imaging (DTI) data were collected using a single-shot echo-planar-imaging with twice-refocused spin-echo pulse sequence (TR = 10,000 ms; TE = 87 ms; FA = 90°; readout bandwidth = 1346 Hz/pixel; matrix size = 128×128; FOV = 230×230 mm; slice thickness = 2.0 mm; no interslice-gap; diffusion gradient directions = 64; b = 0 and 700 s/mm2). The generalized autocalibrating partially parallel acquisition (GRAPPA) parallel imaging technique, with an acceleration factor of two, was used to collect DTI data, and two DTI series were collected individually with the same imaging parameters for subsequent averaging.

Data processing

We used the statistical parametric mapping package SPM8 (http://www.fil.ion.ucl.ac.uk/spm/), DTI-Studio (v 3.0.1, https://www.mristudio.org/) (Jiang et al. 2006), MRIcroN (Rorden et al. 2007), and MATLAB-based (The MathWorks Inc., Natick, MA) custom software for data processing. Proton-density-, T2-, and T1-weighted images were examined visually to ensure no serious anatomical defects were apparent, including cysts, tumors, or any other lesions before data processing. No subjects showed any of these abnormalities on visual examination of brain images. Non-diffusion and diffusion-weighted data were also evaluated for motion and other imaging artifacts to ensure that images were acceptable for subsequent analysis.

Calculation of axial and radial diffusivity indices

The average background noise value outside the brain areas was calculated by assessing absolute signal intensity levels on non-diffusion and diffusion-weighted images, and this value was used to exclude non-brain regions during axial and radial diffusivity calculation, as described earlier (Kumar et al. 2008; Kumar et al. 2010; Kumar et al. 2011b). Using diffusion-weighted images (b = 700 s/mm2), collected from 64 diffusion directions, and non-diffusion images (b = 0 s/mm2), diffusion tensor matrices were calculated with DTI-Studio software (Jiang et al. 2006). The diffusion tensor matrices were diagonalized at each voxel, and three principal eigenvalues (λ1, λ2, and λ3) were derived (Basser and Pierpaoli 1998; Pierpaoli and Basser 1996). Using principal eigenvalues, we calculated axial (λ = λ1) and radial [λ[perpendicular] = (λ2 + λ3)/2] diffusivity maps from each DTI series (Kumar et al. 2008; Kumar et al. 2010; Kumar et al. 2011b; Song et al. 2002; Song et al. 2005).

Realignment, averaging, and normalization

We realigned both axial and radial diffusivity maps, derived from two DTI series of each subject; these maps were averaged to create one axial and one radial diffusivity map for each subject (Kumar et al. 2008; Kumar et al. 2010; Kumar et al. 2011b). Both b0 images (non-diffusion weighted images), derived from both series, were also realigned, and averaged. The averaged axial and radial diffusivity maps were used for subsequent analyses, and b0 images were used for normalization and background purposes.

Both averaged axial and radial diffusivity maps were normalized to Montreal Neurological Institute (MNI) space. Using a priori-defined distributions of gray, white, and cerebrospinal fluid (CSF) tissue types (Ashburner and Friston 2005), b0 images of each subject were normalized to the MNI space template, and the resulting normalization parameters were used to normalize corresponding axial and radial diffusivity maps. The normalized b0 images of all subjects were averaged to create mean background images, which were used to outline regions-of-interest (ROIs) for further analysis.

Region of interest analyses

A set of rectangular ROIs from multiple brain sites were created using mean background images derived from normalized b0 images of all subjects with MRIcroN software. These ROIs were included from multiple brain areas of rostral, thalamic and hypothalamic, pontine, and cerebellar regions (Fig. 1). All ROIs included three consecutive brain slices, and the size of the ROI was selected to fit within the examined structure. Brain sites that were very close to CSF, such as medullary regions, were excluded for evaluation, since small mis-registrations of axial and radial diffusivity maps to MNI space may contaminate axial and radial diffusivity values of those areas.

Fig. 1
Mean background images, derived from normalized and averaged b0 images of all individuals, with regions-of-interest (ROIs). The rectangular ROIs were used to calculate regional axial and radial diffusivity values, which are shown only for the left side ...

Rostral brain areas

Regions assessed within the rostral brain included cortical gray and white matter, amygdala, basal ganglia, and hippocampus. Bilateral structures, including the anterior, mid, and posterior cingulate and insular cortices, caudate nuclei, putamen, globus pallidus, frontal white and gray matter, amygdala, ventral, mid, and dorsal hippocampus and temporal white matter, midline occipital gray matter, and occipital white matter, were assessed. Other unilateral sites, including the anterior, mid, and posterior corpus callosum, were also examined.

Thalamic and hypothalamic regions

Both thalamic and hypothalamic areas were examined. The ROIs were outlined within the left and right hypothalamic regions and thalamic areas, including anterior, mid, and posterior portions of those structures.

Pontine and cerebellar structures

Unilateral and bilateral ROIs were outlined within the pons and cerebellum. Unilateral ROIs were delineated within the ventral, mid, and caudal pons, and cerebellar deep nuclei, and bilateral ROIs were created within the caudal and rostral cerebellar cortices, and inferior, mid, and superior cerebellar peduncles.

Mean axial and radial diffusivity calculation

Mean axial and radial diffusivity values of different brain sites were determined using ROI brain masks of those areas and normalized axial and radial diffusivity maps, as described previously with different data (Kumar et al. 2011a). Axial and radial diffusivity values of the different brain sites were assessed for age-related changes; values of these sites were also compared between genders to determine male-female differences.

Statistical analyses

The Statistical Package for the Social Sciences (SPSS V 18.0, Chicago, IL, USA) software was used for statistical evaluation of data. We used Pearson’s correlation procedures to examine relationships between combined male and female axial and radial diffusivity values of different brain regions with age. Male-female differences in axial and radial diffusivity values in those regions were evaluated with Student’s t-tests. A p value less than 0.05 was considered statistically significant.


The range of age for all subjects was 8–24 years. No significant differences in age appeared between the male and female groups (males vs. females; 18.5±4.6 vs. 16.5±4.6 years, p = 0.25). Body mass index values of males were also equivalent to females (males vs. females; 22.2±4.9 vs. 20.5±3.7 kg/m2, p = 0.31).

Rostral brain areas

Brain sites within the rostral brain that showed, based on the combined male and female data, decreased and increased axial diffusivity values and decreased radial diffusivity values with age are shown in scatter plots (Figs. 2, ,3).3). Relationships between male and female values with age are also displayed in those plots, but only for those sites that showed significant relationships between combined data and age (Figs. 2, ,3).3). Bilateral structures, based on combined male and female data, including the caudate nuclei (left, r = − 0.46, p = 0.01; right, r = − 0.50, p = 0.005) and frontal white matter (left, r = − 0.58, p = 0.001; right, r = − 0.54, p = 0.002), showed negative relationships between axial diffusivity and age; caudate nuclei (left, r = − 0.56, p = 0.001; right, r = − 0.50, p = 0.005), putamen (left, r = − 0.72, p < 0.001; right, r = − 0.62, p < 0.001), mid cingulated (left, r = − 0.38, p = 0.04; right, r = − 0.39, p = 0.03), and globus pallidus (left, r = − 0.50, p = 0.005; right, r = − 0.37, p = 0.04) also showed inverse correlations between radial diffusivity values and age. Other unilateral brain sites that showed negative correlations between axial diffusivity and age in combined male and female data included anterior (r = − 0.53, p = 0.003) and posterior (r = − 0.37, p = 0.04) corpus callosum, left globus pallidus (r = − 0.37, p = 0.04), and right ventral (r = − 0.53, p = 0.002) and left mid (r = − 0.42, p = 0.02) and dorsal (r = − 0.39, p = 0.03) temporal white matter, while positive correlations between axial diffusivity and age included the right mid hippocampus (r = 0.41, p = 0.02). Unilateral brain areas showing negative correlations between radial diffusivity in combined male and female data and age included the left anterior insula (r = − 0.42, p = 0.02), right frontal (r = − 0.40, p = 0.03), and mid (r = − 0.49, p = 0.006) and dorsal temporal (r = − 0.43, p = 0.02) white matter.

Fig. 2
Correlations between combined, and separately, male, and female axial and radial diffusivity values, derived from multiple rostral brain sites, and age. Corresponding sold and dotted lines on the scatter plots display best fit lines for the combined, ...
Fig. 3
Rostral brain sites showing correlations between combined, male, and female axial and radial diffusivity values and age. Figure conventions are the same as in Fig. 2.

Thalamic and hypothalamic regions

Multiple brain sites within thalamic areas showed decreased axial and radial diffusivity values in combined data with age (Fig. 4). Brain sites that showed inverse relationships between axial diffusivity in combined data and age included the left anterior (r = − 0.49, p = 0.006) and right mid (r = − 0.44, p = 0.01) and posterior (r = − 0.44, p = 0.01) thalamus, and between radial diffusivity and age included the bilateral mid thalamus (left, r = − 0.52, p = 0.004; right, r = − 0.56, p = 0.001).

Fig. 4
Thalamic, pontine, and cerebellar regions showing correlations between combined, male, and female, axial and radial diffusivity values and age. Figure conventions are the same as in Fig. 2.

Pontine and cerebellar structures

The mid pons (r = − 0.49, p = 0.006) and bilateral mid cerebellar peduncles (left, r = − 0.44, p = 0.01; right, r = − 0.44, p = 0.02) showed negative correlations between axial diffusivity in combined data and age (Fig. 4). The mid pons (r = − 0.44, p = 0.01) and right rostral cerebellar cortex (r = − 0.49, p = 0.006) showed inverse relationships between radial diffusivity values in combined data and age (Fig. 4).

Male-female differences

Multiple brain regions showed axial and radial diffusivity differences between sexes (Table 13). Brain areas that showed axial diffusivity differences between males and females included the left putamen (p = 0.002), left mid temporal white matter (p = 0.02), left amygdala (p = 0.02), and right mid cerebellar peduncles (p = 0.007). Radial diffusivity differences between genders emerged in the left ventral hippocampus (p = 0.03), left amygdala (p = 0.02), right midline occipital gray matter (p = 0.03), left mid thalamus (p = 0.04), right mid cingulate (0.02), right hypothalamus (p = 0.02), and right rostral cerebellar cortex (p = 0.04).

Table 1
Axial and radial diffusivity values (×10−3 mm2/sec) of various sites within the rostral brain, and male-female differences.
Table 3
Axial and radial diffusivity values (×10−3 mm2/sec) of brain sites within the cerebellum and pons, and male-female differences in those areas.



Multiple brain sites in developing subjects showed negative relationships between axial and radial diffusivity values and age, except for the mid hippocampus where axial diffusivity was positively correlated with age. Gender differences emerged in multiple brain areas, including the basal ganglia, portions of the limbic system, temporal and occipital lobes, thalamus and hypothalamus, and cerebellar sites. Normative axial and radial diffusivity values from regions across the brain will assist comparisons with disease-related tissue changes. Male-female differences and age-related changes in axial and radial diffusivity values in several brain sites suggest the need for controlling these confounds during any evaluation for pathology.

Diffusion tensor imaging and tissue characteristics

Diffusion tensor imaging assesses diffusion of water molecules within the tissue and evaluates tissue changes. The procedure has been used to examine brain tissue changes in developmental studies (Mukherjee and McKinstry 2006; Neil et al. 1998), neurodegenerative diseases, including myelin-related diseases such as Krabbe disease (Guo et al. 2001), dementia (Larsson et al. 2004), and multiple sclerosis (Filippi et al. 2001), and various neurological conditions, including stroke (Werring et al. 2000), and traumatic brain injury (Huisman et al. 2004). The procedure primarily evaluates myelin and axonal characteristics; however, other aspects of fiber characteristics, such as orientation coherence, number and packing of axons, axonal caliber, membrane permeability, and presence of other cells such as glia, can also contribute to DTI changes in ordered axonal systems (Le Bihan et al. 2001; Le Bihan et al. 1993). Changes in cell body characteristics may contribute to DTI changes in less organized tissue, such as cortical regions. High anisotropic diffusion and low mean diffusivity or apparent diffusion coefficient have been observed in brain cystic lesions (Gupta et al. 2005), normal developing cortical brain regions (Miller et al. 2003), and epidermoid tumors (Koot et al. 2003).

Axial and radial diffusivity changes with age

Multiple brain regions showed negative correlations between axial and radial diffusivity values in combined male and female data and age. Since axial diffusivity measures diffusion of water parallel to tissue fibers, principally indicating axonal status (Song et al. 2002; Song et al. 2005), reduced axial diffusivity with age may result from increased numbers of brain fibers or increased axonal caliber in those areas allowing fibers to become less straight due to reduced inter-axonal space (Mukherjee et al. 2002; Qiu et al. 2008; Suzuki et al. 2003; Takahashi et al. 2000).

Since radial diffusivity measures diffusion of water perpendicular to fibers, values mainly reflect myelin changes (Song et al. 2002; Song et al. 2005). Reduction in radial diffusivity values with age may result from increased myelin development (Qiu et al. 2008; Suzuki et al. 2003), since thickening of myelin will reduce perpendicular water diffusion.

Myelin and axonal density, as well as axonal arborization, increase with age up to the third decade of life (Sowell et al. 2003; Toga et al. 2006). After the third decade, slow, natural degradation of fiber characteristics appears as aging proceeds (Hasan 2006).

Male-female differences

Several brain regions, including the putamen, amygdala, temporal white matter, and cerebellar peduncles showed increased axial diffusivity, and cingulate, ventral hippocampus, amygdala, midline occipital gray matter, thalamus and hypothalamus, and rostral cerebellar cortex showed increased radial diffusivity in females over males. The relative increase in axial or radial diffusivity values in females suggests reduced axonal density or caliber, and myelin, respectively, and cortical brain sites that showed increased axial or radial diffusivity values indicate less organization in those sites in females. In addition to slower global and regional gray and white matter development in females over males in pediatric subjects (Giedd et al. 1999), it appears that organization of those fibers with respect to the issues related to myelination and axonal development follows a different time course between sexes. Females show less-reduced axial and radial diffusivity at comparable ages over values for males, which, we speculate, may allow for more functional plasticity in females over males. We captured measures of axonal structure and organization at a time period before the maximal peak of axonal development, and demonstrate that major gender differences exist. Such delayed developmental trajectories in several brain sites, including the frontal lobe, cingulate, thalamus, and hypothalamus, are reflected also as reduced tissue changes in adult females over healthy adult males, as measured by voxel-based morphometry procedures (Takahashi et al. 2010). The findings here of differential gender-based myelination and axonal development in developing subjects may contribute to the well-described cognitive and emotional superiority in females over males in this age period (Kring and Gordon 1998; Mann et al. 1990).


Several brain sites showed inverse correlations between axial and radial diffusivity values and age, except for the right mid hippocampus, suggesting increased axonal density, caliber, and myelin with age. Male-female axial and radial diffusivity differences appeared in multiple brain areas within the rostral brain, thalamic and hypothalamic sites, as well as in cerebellar areas. These normal axial and radial diffusivity values, derived from several regions across the brain, give baseline values against which pathological tissue changes can be evaluated. The gender differences in fiber organization in several brain structures may significantly contribute to sex-related functions mediated by these structures. Normal age-related axial and radial diffusivity changes and gender differences in several brain areas make necessary a requirement for partitioning age- and gender-related changes during examination of pathology in these adolescent and young adult subjects.

Table 2
Axial and radial diffusivity values (×10−3 mm2/sec) of brain regions within the thalamus and hypothalamus, and male female differences.


The authors thank Ms. Rebecca Harper and Mr. Edwin M. Valladares for assistance with data collection.

Grant Support: This research was supported by the National Institute of Child Health and Human Development R01 HD-22695.


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