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Michael D. De Bellis, Matcheri S. Keshavan, Sue R. Beers, Julie Hall, Karin Frustaci, Azadeh Masalehdan, Jessica Noll, Amy M. Boring, Sex Differences in Brain Maturation during Childhood and Adolescence, Cerebral Cortex, Volume 11, Issue 6, June 2001, Pages 552–557, //doi.org/10.1093/cercor/11.6.552
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Abstract
Brain development during childhood and adolescence is characterized by both progressive myelination and regressive pruning processes. However, sex differences in brain maturation remain poorly understood. Magnetic resonance imaging was used to examine the relationships between age and sex with cerebral gray and white matter volumes and corpus callosal areas in 118 healthy children and adolescents (61 males and 57 females), aged 6–17 years. Gender groups were similar on measures of age, handedness, socioeconomic status and Full Scale IQ. Significant age-related reductions in cerebral gray and increases in white matter volumes and corpus callosal areas were evident, while intracranial and cerebral volumes did not change significantly. Significant sex by age interactions were seen for cerebral gray and white matter volumes and corpus callosal areas. Specifically, males had more prominent age-related gray matter decreases and white matter volume and corpus callosal area increases compared with females. While these data are from a cross-sectional sample and need to be replicated in a longitudinal study, the findings suggest that there are age-related sex differences in brain maturational processes. The study of age-related sex differences in cerebral pruning and myelination may aid in understanding the mechanism of several developmental neuropsychiatric disorders.
Introduction
As evidenced by post-mortem studies, brain development during childhood and adolescence is characterized by regressive and progressive processes, such as synaptic and axonal pruning (Huttenlocher, 1979) and progressive myelination (Yakovlev and LeCours, 1967). The advent of quantitative magnetic resonance imaging (MRI) studies has advanced the study of child and adolescent brain development in vivo. Findings from crosssectional studies suggest that cerebral gray matter (GM) volumes decrease progressively after age 4 (Jernigan and Tallal, 1990; Jernigan et al., 1991; Pfefferbaum et al., 1994; Caviness et al., 1996; Reiss et al., 1996), perhaps in relationship to the regressive processes of synaptic and axonal pruning during development. Giedd and his colleagues have recently demonstrated in longitudinal studies that there are regionally specific nonlinear pre-adolescent increases followed by post-adolescent decreases in cortical GM (Giedd et al., 1999a; Thompson et al., 2000). On the other hand, findings from cross-sectional studies suggest that cerebral white matter (WM) volume (Pfefferbaum et al., 1994; Caviness et al., 1996; Reiss et al., 1996) and the area of the corpus callosum (CC), the main interhemispheric commissure, increase significantly from childhood through late adolescence (Giedd et al., 1996a). Recent results from longitudinal MRI studies of healthy children and adolescents have confirmed these age-related linear increases in cerebral WM and CC area (Giedd et al., 1999a; Giedd et al., 1999b; Thompson et al., 2000). These observations may reflect in vivo evidence of age-related progressive events such as axonal growth and myelination.
It is well known that cognitive and emotional development differs between boys and girls [for review see (Nagy Jacklin and Martin, 1999)], though the timing, patterning and neurobiological parallels of such differential development remain poorly understood. Normal pubertal development is associated with marked increases in plasma levels of sex steroids (Ducharme and Forest, 1993). Preclinical studies suggest that sex steroid receptors are widely distributed throughout the brain and influence neurodevelopment [for review see (McEwen, 1981)]. Cognitive abilities, particularly visuo-spatial skills, differ between males and females (Hampson and Kimura, 1992; Kimura, 1996; Janowsky et al., 1998). Sex differences in brain development may be related to the prevalence, course and treatment of several neuropsychiatric disorders, such as autism, attention deficit hyperactivity disorder and schizophrenia (Seeman, 1997; Cohen et al., 1999).
We investigated the relationship between age, sex and cerebral GM and WM volumes and CC area using high-resolution MRI volumetric analyses in a large community sample of healthy, age-matched and sociodemographically similar male and female children and adolescents. We specifically investigated age- related sex differences in human brain maturational processes (age-related changes in cerebral GM and WM volumes and CC area).
Materials and Methods
Subjects
Sixty-one male and 57 female healthy children and adolescents (age range: 6.9–17 years) were recruited by advertisement from the community and underwent extensive clinical evaluations. The Schedule for Affective Disorders and Schizophrenia for School Aged Children Present and Lifetime Version (K-SADS-PL), which includes a comprehensive posttraumatic stress disorder interview (Kaufman et al., 1997), ruled out the presence of DSM-IV Axis I mental disorders. Socioeconomic status (SES) for each subject was completed using the Hollingshead Four Factor Index (Hollingshead, 1975). An abbreviated version of the Wechsler Intelligence Scale for Children (WISC-R) (i.e. Vocabulary, Digit Span, Block Design and Object Assembly) provided an estimate of Full Scale IQ (Wechsler, 1974). Handedness was determined using the 12 handedness items from the Revised Physical and Neurological Examination for Subtle Signs (PANESS) Inventory (Denckla, 1985), where eight out of 12 items were defined as right handed. There were no significant group differences on age, race, Tanner stage (Marshall and Tanner, 1969, 1970), SES, handedness, and Full Scale IQ. Males were significantly taller than females. The majority of subjects were above average on Full Scale IQ (median IQ: 116). The demographic characteristics of the groups are presented in Table 1.
Exclusion criteria were: (i) current or lifetime history of psychiatric disorders, including alcohol and substance use disorders; (ii) a significant medical, neurological or psychiatric disorder, or history of head injury or loss of consciousness; (iii) a history of prenatal confounds that may influence brain maturation, such as prenatal exposure to substances or pregnancy and birth complications; (iv) severe obesity or growth failure; (v) Full Scale IQ lower than 80; and (vi) positive trauma or maltreatment history. This study was approved by the Biomedical Institutional Review Board of the University of Pittsburgh. After a complete description of the study was given to the subject and parents, written informed consent was obtained. Subjects received monetary compensation for participation.
Magnetic Resonance Imaging
MRI was performed using a GE 1.5 Tesla Unit (Signa System, General Electric Medical Systems, Milwaukee, WI) running version 5.4 software located at the UPMC MR Research Center. The subject's head was aligned in a head holder. Foam padding was placed on both sides of the head and wrapped soft towels were placed under the chin with the use of chin and forehead straps to minimize head movement. The subject's nose was positioned at ‘12:00’ for alignment in this plane. A gradient multi-echo localizing axial slice verified this plane. A sagittal series (using TE = 18 ms, TR = 400 ms, flip angle = 90°, acquisition matrix = 256 × 192, NEX = 1, FOV =, 20 cm, slices = 21) verified patient position, cooperation and image quality. We required that the midsagittal slice shows full visualization of the cerebral aqueduct and the anterior and posterior commissures, in which a line was estimated requiring the anterior commissure–posterior commissure line to be within 3° of 180. If these criteria were not met, the subject was realigned until this criterion was met. Coronal sections were then obtained perpendicular to the anterior commissure–posterior commissure line to provide a more reproducible guide for image orientation. A three-dimensional spoiled gradient recalled acquisition in the steady-state pulse sequence was used to obtain 124 contiguous images with slice thickness of 1.5 mm in the coronal plane (using TE = 5 ms, TR = 25 ms, flip angle = 40°, acquisition matrix = 256 × 192, NEX = 1, FOV = 24 cm). Axial proton density and T2-weighted images were obtained to enable exclusion of structural abnormalities on MRI. A neuroradiologist reviewed all scans and ruled out clinically significant abnormalities.
Prior to the actual scanning procedure, subjects underwent a desensitization procedure in a simulation scanner, which reproduced the sights and sounds of the scanning environment. This method achieves increased patient cooperation and improvement in image acquisition due to reduced head movement artifact (Rosenberg et al., 1997b). Subjects watched videos of their favorite movies during scanning. Subjects were motivated to remain still by allowing them to see their brain images after their scan (i.e. if they move, their brain pictures would be blurred). Subjects tolerated the procedure well and all scans were obtained with no or minimum head movement artifact (<6%). No sedation was used. Scanning was directly supervised by a child and adolescent psychiatrist (M.D.D.B.).
Image Analysis
The imaging data were transferred from the MRI unit to a computer workstation (PowerMacintosh, Apple Computer) and analyzed using the IMAGE software (version 1.52) developed at the NIH (Rasband, 1996) that provides valid and reliable volume measurements of specific structures using a manually operated (hand tracing) approach. All measurements were made by trained and reliable raters who were blind to subject information. Intraclass correlation of inter- and intrarater reliability for independent designation of regions on segmented images obtained from 20 subjects were 0.99 and 0.99 for intracranial volume (J.H. and K.F.), 0.99 and 0.99 for cerebral volume (J.H. and K.F.), and 0.99 and 0.99, respectively, for cerebral GM and WM volumes (J.H. and K.F.). Intra- and inter-rater reliability from 20 subjects were 0.99 and 0.98, respectively, for total CC area (J.H. and A.M.B., and J.H. and J.N.). These methods were previously described by our group (De Bellis et al., 1999, 2000a, De Bellis et al., b).
Intracranial volumes were calculated by first manually tracing the intracranial volume of each coronal slice after exclusion of skull and dura, then summing these areas of successive coronal slices, including GM and WM and cerebral spinal fluid (CSF) volumes, and multiplying by slice thickness. These measures included frontal, parietal, temporal, occipital cortex, subcortical structures, cerebellum and brainstem.
Cerebral volumes were measured after manual exclusion of CSF volumes, cerebellum and brainstem in the same manner and included cortical and subcortical structures.
Total cerebral GM and WM volumes were calculated using a semi-automated segmentation algorithm. This computerized segmentation technique is both labor intensive and manually operated. It uses an interactive method where mathematically derived cutoffs for GM-WM-CSF partitions from histograms of signal intensities are used to individually select GM, WH, and CSF areas from each coronal slice. GM and WM and CSF areas are thus separately calculated and multiplied by slice thickness for individual subjects' GM, WM, and CSF volumes. In this way, we can minimize the inherent limitations on qualifying WM signal hypointensities as GM on T1-weighted MRI scans by visual inspection of slices (i.e. so that hypointensity artifacts in the CC or cerebral WM are not calculated as GM). This approach has been validated using both a stereological technique for brain morphometric measurements and a phantom with known absolute volumes (Keshavan et al., 1995), and has been used in several published neuroimaging studies (Keshavan et al., 1994b; Rosenberg et al., 1997a; De Bellis et al., 1999). Total cerebral GM and WH volume measures included cortical and subcortical WM and GM volumes.
The CC area was measured from a single midsagittal section selected as the slice showing full visualization of the anterior and posterior commissures and the cerebral aqueduct [as described in a diagram published previously (De Bellis et al., 1999)].
Data Analysis
Data distributions were examined for normality and outliers before applying linear models. Linear regression models were undertaken for gender groups, age, and age by group interactions, and for the main effects of group and age on the dependent variables using covariates as described. To control for the known sex differences, cerebral volume was covaried for GM, WH and CC analyses. All significance testing involving the main hypothesis was two-tailed, with alpha < 0.05.
Results
Age-related Changes between Genders
The sex by age interaction term was significant for cerebral GM and WM volumes and CC area (see Fig. 1). The slopes of these changes significantly differed between male and female subjects (see Table 2). Thus girls showed significant developmental changes with age but at a slower rate than boys. Specifically, males had an ~19.1% reduction in GM volume between 6 and 18 years of age compared with a 4.7% reduction in females. On the other hand, males had a 45.1% increase in WM and a 58.5% increase in CC area compared with 17.1 and 27.4% increases, respectively, in females.
After covarying for the effects of Full Scale IQ, the sex by age interaction term remained significant for cerebral GM [F(1,112) = 7.00, P = 0.009] and WM [F(1,112) = 7.18, P < 0.009] volumes. A suggestive but nonsignificant difference was seen in the sex by age interaction term for CC area [F(1,112) = 3.69, P < 0.06].
To explore the relationship between Tanner stage of pubertal development and cerebral GM and WM volumes and CC area, separate analyses were undertaken by substituting Tanner stage for age. In this case, the sex by Tanner stage interaction term were significant for cerebral GM [F(1,107) = 2.75, P = 0.03] and cerebral WM volumes [F(1,107) = 2.78, P = 0.03] and CC area [F(1,107) = 2.43, P = 0.05].
Age
Overall, cerebral GM volume showed the expected significant decrease, while cerebral WM volume and corpus callosal area showed the expected significant increase with age (see Fig. 1 and Table 2). Goodness-of-fit regression models of these structures indicated that the relationships with age were linear. Intracranial and cerebral volumes did not significantly increase with age (see Fig. 1).
Gender
Males had larger intracranial and cerebral volumes than females by 11 and 12%, respectively [F(1,115) = 55.90, P < 0.0001]. These effects remained after correction for height [F(1,115) = 57.69, P < 0.0001]. Cerebral GM and WM volumes and CC areas did not differ between gender groups after adjustment for cerebral volumes (see Table 2).
Discussion
In this neuroimaging study, boys showed significantly greater loss of GM volume and an increase in both WM and CC area compared with girls over a similar age range. Consequently, girls showed significant developmental changes with age but at a slower rate than boys. Consistent with earlier cross-sectional studies (Jernigan and Tallal, 1990; Jernigan et al., 1991; Pfefferbaum et al., 1994; Caviness et al., 1996; Giedd et al., 1996a, b; Reiss et al., 1996) and recent longitudinal investigations (Giedd et al., 1999b; Rapoport et al., 1999; Thompson et al., 2000), significant age-related decreases in cerebral GM and increases in cerebral WM volumes and CC areas were evident in the overall sample, while intracranial and cerebral volumes did not change significantly. While these data are from a crosssectional sample and need to be replicated in a longitudinal study, the results suggest that there are age-related sex differences in brain maturation. GM decreases are likely to reflect dendritic pruning processes, since GM is largely composed of cells and dendrites; there is no evidence of large scale cell loss (apoptosis) during late childhood and adolescence (Oborai et al., 1998). WM increases could be due either to myelination, increases in axonal size, glial proliferation or a combination of these.
To our knowledge, this is the first study showing sex differences in both cerebral GM and WM maturational processes in childhood and adolescence. Results from a recent longitudinal MRI study of child and adolescent brain development also described sex differences in the rate of linear WM increase, with greater age-related increases in males than females (Giedd et al., 1999a). These results are similar to those of this cross-sectional study reported here. Giedd et al. (Giedd et al., 1999a) did not find sex differences in the nonlinear rate of pre-adolescent increases and post-adolescent decreases seen in regional GM over this developmental period. Subcortical GM measures were not included in this latter study, which may have contributed to the differences in findings. However, several cross-sectional investigations of human aging have suggested that there may be greater age-related atrophy in males compared with females (Gur et al., 1991; Kaye et al., 1992; Cowell et al., 1994; Murphy et al., 1996; Coffey et al., 1998; Xu et al., 2000). Significant sex by age interactions were found for sex differences in regional volumes in two of these studies (Cowell et al., 1994; Murphy et al., 1996). Recently, significantly larger ventricular volumes and smaller cerebral GM and WM volumes in older compared with younger people and in men compared with women were reported in a cross-sectional study of 116 adults, aged 59–85 years (Resnick et al., 2000). However, significant sex by age interactions were not seen and no detectable changes upon repeat MRI on cerebral GM and WM volumes measures after 1 year follow-up were seen in this restricted age range (Resnick et al., 2000).
Previous cross-sectional MRI studies of children showed no significant sex differences in the slopes or shapes of linear or higher-order age functions for cerebral GM and WM volume and CC area measures (Jernigan and Tallal, 1990; Jernigan et al., 1991; Pfefferbaum et al., 1994; Giedd et al., 1996a; Reiss et al., 1996). Relatively small sample sizes, the known wide range of inter-individual variation in cerebral structures, unequal samples of boys and girls, the use of clinic based populations as controls and the wide age range of subjects studied may have contributed to these negative findings.
Normal pubertal development is associated with a 26-fold increase in testosterone plasma levels in males and a 10-old increase in estradiol plasma levels in females (Ducharme and Forest, 1993). Findings from animal studies suggest that sex steroids influence neurodevelopment. Estradiol positively influences hippocampal cell proliferation (Tanapat et al., 1999), number of dendritic spines (Gould et al., 1990) and synaptogenesis (Woolley et al., 1996), and delays synaptic pruning in other brain regions (Naftolin et al., 1990). On the other hand, testosterone (Martini and Melcangi, 1991) may be associated with myelinogenesis. The results from this study may suggest that the earlier maturation in females may lead to an estrogen- mediated delay in dendritic pruning. The findings reported here, of significant sex by Tanner stage interaction terms for cerebral GM and cerebral WM volumes and CC area, support the idea of a hormonal influence on these brain maturational processes.
The results of this study are limited by its cross-sectional design and need to be replicated with longitudinal data to truly demonstrate if the growth curves are different for boys and girls. Other limitations of this investigation may have contributed to these findings. These include (i) a wide range of inter-individual variation in cerebral structures between subjects of similar age; (ii) the use of highly functioning subjects, whose results may not be generalizable; and (iii) the use of a methodological approach that only estimates gray and WM tissue separation as there are no universally accepted or validated GM–WM separation methods published to date. However, the inclusion of a large group of healthy, high functioning and clinically well characterized child and adolescent subjects, who were well matched on important variables and very cooperative during the scanning procedures, was an important strength in this study.
Thus, if these findings are confirmed with longitudinal data, the observed differences in patterns of brain maturation between boys and girls are likely to be of considerable pathophysiological significance in neuropsychiatry. It has been proposed that neuropsychiatric illnesses, such as schizophrenia, with a typical adolescent onset may be mediated by excess elimination of synapses (Feinberg, 1982; Keshavan et al., 1994a). This theory has received recent support from neuropathological studies (Glantz and Lewis, 2000). The rapid rate of periadolescent pruning in males may underlie the early age of onset and increased illness severity in male schizophrenic patients (Seeman, 1997; Cohen et al., 1999). It was also found that maltreated male children and adolescents with post-traumatic stress disorder showed more evidence of adverse brain development than maltreated females with post-traumatic stress disorder, an illness that may be seen as environmentally induced (De Bellis et al., 1999). Further research is needed to examine whether estrogens have a protective effect against certain neuropsychiatric disorders (i.e. schizophrenia or post-traumatic stress disorder) and whether this is mediated by the hormonal effects on cerebral pruning processes. Age-related sex differences in cerebral pruning and myelination warrant further investigation and may aid in understanding the mechanism of several developmental neuropsychiatric disorders.
Notes
This work was supported by NARSAD Young Investigator Awards (Principal Investigator: M.D.D.B.), by NIMH grant 5 K08 MHO1324-02 (M.D.D.B.) and in parts by NIMH grants MH01180 and MH43687 (M.S.K.), NIMH grant MH 41712 (Principal Investigator: Neal D. Ryan) and NIAAA grant AA08746-08 (Principal Investigator: Dr Clark). The authors thank Grace Moritz, Cara Renzelli, Duncan Clark and Neal D. Ryan for their assistance in this work, and Douglas E. Williamson for statistical consultation.
Address correspondence to Michael D. De Bellis, Associate Professor of Psychiatry, University of Pittsburgh Medical Center, Director, Developmental Traumatology Neuroimaging Laboratory, Western Psychiatric Institute and Clinic, 3811 O'Hara Street, Pittsburgh, PA 15213, USA. Email: .
Table 1
Demographic characteristics of healthy male and female child and adolescent MRI subjects
| SES = socioeconomic status; Z = Wilcoxon/Kruskal–Wallis test. | ||||
| n | 61 | 57 | – | – |
| Age in years (range in years) | 12.0 ± 2.3 (6.9–17.0) | 11.8 ± 2.5 (7.3–16.3) | t116 = 0.51 | 0.61 |
| Race: white/biracial/African American | 51/6/4 | 40/12/5 | Fisher's exact test | NS |
| Tanner stage I/ II/III/IV/V | 17/19/13/8/4 | 18/12/15/8/4 | χ2 = 1.62 | 0.81 |
| Weight (kg) | 51.1 ± 18.1 | 44.9 ± 17.0 | Z1 = 1.91 | <0.06 |
| Height (cm) | 156.2 ± 15.1 | 148.9 ± 16.6 | t116 = 2.50 | <0.02 |
| SES (range) | 42.0 ± 10.8 (18–64) | 39.5 ± 8.5 (18–58) | Z1 = 0.89 | 0.37 |
| Handedness (right/left) | 58/3 | 55/2 | Fisher's exact test | NS |
| Full Scale IQ (range) | 119.2 ± 17.0 (89–153) | 117.0 ± 13.1 (90–145) | Z1 = 0.79 | 0.43 |
| SES = socioeconomic status; Z = Wilcoxon/Kruskal–Wallis test. | ||||
| n | 61 | 57 | – | – |
| Age in years (range in years) | 12.0 ± 2.3 (6.9–17.0) | 11.8 ± 2.5 (7.3–16.3) | t116 = 0.51 | 0.61 |
| Race: white/biracial/African American | 51/6/4 | 40/12/5 | Fisher's exact test | NS |
| Tanner stage I/ II/III/IV/V | 17/19/13/8/4 | 18/12/15/8/4 | χ2 = 1.62 | 0.81 |
| Weight (kg) | 51.1 ± 18.1 | 44.9 ± 17.0 | Z1 = 1.91 | <0.06 |
| Height (cm) | 156.2 ± 15.1 | 148.9 ± 16.6 | t116 = 2.50 | <0.02 |
| SES (range) | 42.0 ± 10.8 (18–64) | 39.5 ± 8.5 (18–58) | Z1 = 0.89 | 0.37 |
| Handedness (right/left) | 58/3 | 55/2 | Fisher's exact test | NS |
| Full Scale IQ (range) | 119.2 ± 17.0 (89–153) | 117.0 ± 13.1 (90–145) | Z1 = 0.79 | 0.43 |
Table 1
Demographic characteristics of healthy male and female child and adolescent MRI subjects
| SES = socioeconomic status; Z = Wilcoxon/Kruskal–Wallis test. | ||||
| n | 61 | 57 | – | – |
| Age in years (range in years) | 12.0 ± 2.3 (6.9–17.0) | 11.8 ± 2.5 (7.3–16.3) | t116 = 0.51 | 0.61 |
| Race: white/biracial/African American | 51/6/4 | 40/12/5 | Fisher's exact test | NS |
| Tanner stage I/ II/III/IV/V | 17/19/13/8/4 | 18/12/15/8/4 | χ2 = 1.62 | 0.81 |
| Weight (kg) | 51.1 ± 18.1 | 44.9 ± 17.0 | Z1 = 1.91 | <0.06 |
| Height (cm) | 156.2 ± 15.1 | 148.9 ± 16.6 | t116 = 2.50 | <0.02 |
| SES (range) | 42.0 ± 10.8 (18–64) | 39.5 ± 8.5 (18–58) | Z1 = 0.89 | 0.37 |
| Handedness (right/left) | 58/3 | 55/2 | Fisher's exact test | NS |
| Full Scale IQ (range) | 119.2 ± 17.0 (89–153) | 117.0 ± 13.1 (90–145) | Z1 = 0.79 | 0.43 |
| SES = socioeconomic status; Z = Wilcoxon/Kruskal–Wallis test. | ||||
| n | 61 | 57 | – | – |
| Age in years (range in years) | 12.0 ± 2.3 (6.9–17.0) | 11.8 ± 2.5 (7.3–16.3) | t116 = 0.51 | 0.61 |
| Race: white/biracial/African American | 51/6/4 | 40/12/5 | Fisher's exact test | NS |
| Tanner stage I/ II/III/IV/V | 17/19/13/8/4 | 18/12/15/8/4 | χ2 = 1.62 | 0.81 |
| Weight (kg) | 51.1 ± 18.1 | 44.9 ± 17.0 | Z1 = 1.91 | <0.06 |
| Height (cm) | 156.2 ± 15.1 | 148.9 ± 16.6 | t116 = 2.50 | <0.02 |
| SES (range) | 42.0 ± 10.8 (18–64) | 39.5 ± 8.5 (18–58) | Z1 = 0.89 | 0.37 |
| Handedness (right/left) | 58/3 | 55/2 | Fisher's exact test | NS |
| Full Scale IQ (range) | 119.2 ± 17.0 (89–153) | 117.0 ± 13.1 (90–145) | Z1 = 0.79 | 0.43 |
Table 2
Means and linear regression of brain structures of healthy male and female child and adolescent MRI subjects
| aLinear regression adjusting for sex, age and cerebral volume. | |||||
| bLinear regression adjusting for sex, age, sex and age interaction, and cerebral volume. | |||||
| Intracranial volume (cm3) | 1581.94 ± 137.54 | 1409.86 ± 92.64 | F(1,115) = 63.03; P < 0.0001 | F(1,115) = 0.86; P = 0.35 | |
| Cerebral volume | 1344.53 ± 123.41 | 1188.50 ± 85.70 | F(1,115) = 63.49; P < 0.0001 | F(1,115) = 0.13; P = 0.72 | |
| Cerebral GM | 850.75 ± 97.41 | 767.96 ± 71.50 | F(1,114) = 1.72a; P = 0.19 | F(1,114) = 25.07a; P < 0.0001 | F(1,113) = 8.13b; P = 0.005 |
| Cerebral WM | 493.79 ± 80.79 | 420.34 ± 49.52 | F(1,114) = 1.41a; P = 0.24 | F(1,114) = 23.44a; P < 0.0001 | F(1,113) = 7.32b; P < 0.008 |
| CC (cm2) | 8.04 ± 1.28 | 7.69 ± 0.93 | F(1,114) = 0.13a; P = 0.72 | F(1,114) = 33.27a; P < 0.0001 | F(1,113) = 3.94b; P < 0.05 |
| aLinear regression adjusting for sex, age and cerebral volume. | |||||
| bLinear regression adjusting for sex, age, sex and age interaction, and cerebral volume. | |||||
| Intracranial volume (cm3) | 1581.94 ± 137.54 | 1409.86 ± 92.64 | F(1,115) = 63.03; P < 0.0001 | F(1,115) = 0.86; P = 0.35 | |
| Cerebral volume | 1344.53 ± 123.41 | 1188.50 ± 85.70 | F(1,115) = 63.49; P < 0.0001 | F(1,115) = 0.13; P = 0.72 | |
| Cerebral GM | 850.75 ± 97.41 | 767.96 ± 71.50 | F(1,114) = 1.72a; P = 0.19 | F(1,114) = 25.07a; P < 0.0001 | F(1,113) = 8.13b; P = 0.005 |
| Cerebral WM | 493.79 ± 80.79 | 420.34 ± 49.52 | F(1,114) = 1.41a; P = 0.24 | F(1,114) = 23.44a; P < 0.0001 | F(1,113) = 7.32b; P < 0.008 |
| CC (cm2) | 8.04 ± 1.28 | 7.69 ± 0.93 | F(1,114) = 0.13a; P = 0.72 | F(1,114) = 33.27a; P < 0.0001 | F(1,113) = 3.94b; P < 0.05 |
Table 2
Means and linear regression of brain structures of healthy male and female child and adolescent MRI subjects
| aLinear regression adjusting for sex, age and cerebral volume. | |||||
| bLinear regression adjusting for sex, age, sex and age interaction, and cerebral volume. | |||||
| Intracranial volume (cm3) | 1581.94 ± 137.54 | 1409.86 ± 92.64 | F(1,115) = 63.03; P < 0.0001 | F(1,115) = 0.86; P = 0.35 | |
| Cerebral volume | 1344.53 ± 123.41 | 1188.50 ± 85.70 | F(1,115) = 63.49; P < 0.0001 | F(1,115) = 0.13; P = 0.72 | |
| Cerebral GM | 850.75 ± 97.41 | 767.96 ± 71.50 | F(1,114) = 1.72a; P = 0.19 | F(1,114) = 25.07a; P < 0.0001 | F(1,113) = 8.13b; P = 0.005 |
| Cerebral WM | 493.79 ± 80.79 | 420.34 ± 49.52 | F(1,114) = 1.41a; P = 0.24 | F(1,114) = 23.44a; P < 0.0001 | F(1,113) = 7.32b; P < 0.008 |
| CC (cm2) | 8.04 ± 1.28 | 7.69 ± 0.93 | F(1,114) = 0.13a; P = 0.72 | F(1,114) = 33.27a; P < 0.0001 | F(1,113) = 3.94b; P < 0.05 |
| aLinear regression adjusting for sex, age and cerebral volume. | |||||
| bLinear regression adjusting for sex, age, sex and age interaction, and cerebral volume. | |||||
| Intracranial volume (cm3) | 1581.94 ± 137.54 | 1409.86 ± 92.64 | F(1,115) = 63.03; P < 0.0001 | F(1,115) = 0.86; P = 0.35 | |
| Cerebral volume | 1344.53 ± 123.41 | 1188.50 ± 85.70 | F(1,115) = 63.49; P < 0.0001 | F(1,115) = 0.13; P = 0.72 | |
| Cerebral GM | 850.75 ± 97.41 | 767.96 ± 71.50 | F(1,114) = 1.72a; P = 0.19 | F(1,114) = 25.07a; P < 0.0001 | F(1,113) = 8.13b; P = 0.005 |
| Cerebral WM | 493.79 ± 80.79 | 420.34 ± 49.52 | F(1,114) = 1.41a; P = 0.24 | F(1,114) = 23.44a; P < 0.0001 | F(1,113) = 7.32b; P < 0.008 |
| CC (cm2) | 8.04 ± 1.28 | 7.69 ± 0.93 | F(1,114) = 0.13a; P = 0.72 | F(1,114) = 33.27a; P < 0.0001 | F(1,113) = 3.94b; P < 0.05 |
Figure 1.
Scatterplots of cerebral volume (A), cerebral GM (B) and cerebral WM (C) volumes and CC areas (D) by age and sex in healthy male (n = 61) (solid lines, individual points = Y) and female (n = 57) (dashed lines, individual points = X) children and adolescents. Cerebral GM and WM volume and CC area means were adjusted for cerebral volume. For males, the linear regression lines with regression equation and R2 were: cerebral vol. = 1342.75 + 0.14768 age, R2= 0.00; GM = 1013.89 – 17.2084 age, R2 = 0.35; GM = 300.208 + 13.519 age, R2 = 0.24; and CC = 4.34662 + 0.2962 age, R2 = 0.27. For females, the linear regression lines with regression equation and R2 were: cerebral vol. = 1158.53 + 2.5461 age, R2 = 0.005; GM = 876.772 – 5.30581 age, R2 = 0.08; WM = 407.774 + 3.92078 age, R2 = 0.06; and CC = 6.10137 + 0.14767 age, R2 = 0.17.
References
Caviness VS Jr, Kennedy DN, Richelme C, Rademacher J, Filipek PA (
1996
) The human brain age 7–11 years: a volumetric analysis based on magnetic resonance images. Cereb
Cortex
6
:
726
–736.
Coffey CE, Lucke JF, Saxton JA, Ratcliff G, Billig B, Bryan RN (
1998
) Sex differences in brain aging: a quantitative magnetic resonance imaging study.
Arch Neurol
55
:
169
–179.
Cohen RZ, Seeman MV, Gotowiec A, Kopala L (
1999
) Earlier puberty as a predictor of later onset of schizophrenia in women.
Am J Psychiat
156
:
1059
–1064.
Cowell PE, Turetsky BI, Gur RC, Grossman RI, Shtasel DL, Gur RE (
1994
) Sex differences in aging of the human frontal and temporal lobes.
J Neurosci
14
:
4748
–4755.
De Bellis MD, Clark DB, Beers SR, Soloff P, Boring AM, Hall J, Kersh A, Keshavan MS (
2000
) Hippocampal volume in adolescent onset alcohol use disorders.
Am J Psychiat
157
:
737
–744.
De Bellis MD, Keshavan M, Clark DB, Casey BJ, Giedd J, Boring AM, Frustaci K, Ryan ND (
1999
) A.E. Bennett Research Award. Developmental traumatology, Part II. Brain development.
Biol Psychiat
45
:
1271
–1284.
De Bellis MD, Keshavan MS, Spencer S, Hall J (
2000
) N-Acetylaspartate concentration in the anterior cingulate in maltreated children and adolescents with PTSD.
Am J Psychiat
157
:
1175
–1177.
Denckla MB (
1985
) Revised physical and neurological examination for soft signs.
Psychopharmacol Bull
21
:
773
–800.
Ducharme JR, Forest MG (1993) Normal pubertal development. In: Pediatric endocrinology: physiology, pathophysiology, and clinical aspects, 2nd edn (Bertrand J, Rappaport R, Sizonenko PC, eds), pp. 372–386. Baltimore, MD: Williams & Wilkins,
Feinberg I (
1982
) Schizophrenia: caused by a fault in programmed synaptic elimination during adolescence?
J Psychiat Res
17
:
319
–334.
Giedd JN, Blumenthal J, Jeffries NO, Castellanos X, Liu H, Zijdenbos A, Paus T, Evans AC, Rapoport JL (
1999
) Brain development during childhood and adolescence: a longitudinal MRI study.
Nature Neurosci
2
:
861
–863.
Giedd JN, Blumenthal J, Jeffries NO, Rajapakse JC, Vaituzis AC, Liu H, Berry YC, Tobin M, Nelson J, Castellanos FX (
1999
) Development of the human corpus callosum during childhood and adolescence: a longitudinal MRI study. Prog.
Neuro-Psychopharmacol Biol Psychiat
23
:
571
–588.
Giedd JN, Rumsey JM, Castellanos FX, Rajapakse JC, Kaysen D, Vaituzis AC, Vauss YC, Hamburger SD, Rapoport JL (
1996
) A quantitative MRI study of the corpus callosum in children and adolescents.
Devl Brain Res
91
:
274
–280.
Giedd JN, Snell JW, Lange N, Rajapakse JC, Kaysen D, Vaituzis AC, Vauss YC, Hamburger SD, Kouch PL, Rapoport JL (
1996
) Quantitative magnetic resonance imaging of human brain development: ages 4–18.
Cereb Cortex
6
:
551
–560.
Glantz LA, Lewis DA (
2000
) Decreased dendritic spine density on prefrontal cortical pyramidal neurons in schizophrenia.
Arch Gen Psychiat
57
:
65
–73.
Gould E, Woolley CS, Frankfurt M, McEwen BS (
1990
) Gonadal steroids regulate dendritic spine density in hippocampal cells in adulthood.
J Neurosci
10
:
1286
–1291.
Gur RC, Mozley PD, Resnick SM, Gottlieb G, Kohn M, Zimmerman RA, Herman G, Atlas S, Grossman R, Berretta DA, Erwin R, Gur RE (
1991
) Gender differences in age effect on brain atrophy measured by magnetic resonance imaging.
Proc Natl Acad Sci USA
88
:
2845
–2849.
Hampson E, Kimura D (1992) Sex differences and hormonal influences on cognitive function in humans. In: Behavioral endocrinology (Becker JB, Breedlove JB, Crews D, eds), pp. 357–397. Cambridge, MA: MIT Press.
Hollingshead AB (1975) Four factor index of social status. New Haven, CT: Hollingshead
Huttenlocher PR (
1979
) Synaptic density in human frontal cortex — developmental changes and effects of aging.
Brain Res
163
:
195
–205.
Janowsky JS, Chaves B, Zamboni BD, Orwoll E (
1998
) The cognitive neuropsychology of sex hormones in men and women.
Devl Neuropsychol
14
:
421
–440.
Jernigan TL, Tallal P (
1990
) Late childhood changes in brain morphology observable with MRI.
Devl Med Child Neurol
32
:
379
–385.
Jernigan TL, Trauner DA, Hesselink JR, Tallal P (
1991
) Maturation of human cerebrum observed in vivo during adolescence.
Brain
114
:
2037
–2049.
Kaufman J, Birmaher B, Brent D, Rao U, Flynn C, Moreci P, Williamson D, Ryan N (
1997
) Schedule for affective disorders and schizophrenia for school-age children — present and lifetime version (K-SADS-PL). Initial reliability and validity data.
J Am Acad Child Adolesc Psychiat
36
:
980
–988.
Kaye JA, DeCarli C, Luxenberg JS, Rapoport SI (
1992
) The significance of age-related enlargement of the cerebral ventricles in healthy men and women measured by quantitative computed x-ray tomography.
Am J Geriat Soc
40
:
225
–231.
Keshavan MS, Anderson S, Beckwith C, Nash K, Pettegrew JW, Krishman RR (
1995
) A comparison of stereology and segmentation techniques for volumetric measurements of lateral ventricles in magnetic resonance imaging.
Psychiat Res : Neuroimag
61
:
53
–60.
Keshavan MS, Anderson S, Pettegrew JW (
1994
) Is schizophrenia due to excessive synaptic pruning in the prefrontal cortex? The Feinberg hypothesis revisited.
J Psychiat Res
28
:
239
–265.
Keshavan MS, Beckwith C, Bagwell W, Pettegrew JW, Krishman RR (
1994
) An objective method for edge detection in MRI morphometry.
Eur J Psychiat
9
:
205
–207.
Kimura D (
1996
) Sex, sexual orientation and sex hormones influence human cognitive function.
Curr Opin Neurobiol
6
:
259
–263.
Marshall WA, Tanner JM (
1969
) Variations in pattern of pubertal changes in girls.
Arch Dis Childh
44
:
291
–293.
Marshall WA, Tanner JM (
1970
) Variations in pattern of pubertal changes in boys.
Arch Dis Childh
45
:
13
–23.
Martini L, Melcangi RC (
1991
) Androgen metabolism in the brain.
J Steroid Biochem Mol Biol
39
:
819
–828.
McEwen BS (
1981
) Neural gonadal steroid actions.
Science
211
:
1303
–1311.
Murphy DGM, DeCarli C, McIntosh AR, Daly E, Mentis MJ, Pietrini P, Szczepanik J, Schapiro MB, Grady CL, Horwitz B, Rapoport SI (
1996
) Sex differences in human brain morphometry and metabolism: an in vivo quantitative magnetic resonance imaging and positron emission tomography study on the effects of aging.
Arch Gen Psychiat
53
:
585
–594.
Naftolin F, Garcia-Segura LM, Keefe D, Leranth C, Maclusky NJ, Brawer JR (
1990
) Estrogen effects on the synaptology and neural membranes of the rat hypothalamic arcuate nucleus.
Biol Reprod
42
:
21
–28.
Nagy Jacklin C, Martin LJ (1999) Effects of gender on behavior and development. In: Developmental–behavioral pediatrics, 3rd edn (Levine MD, Carey WB, Crocker AC, eds), pp. 100–106. Philadelphia, PA: W.B. Saunders.
Oborai T, Mizuquchi M, Takashima S (
1998
) Developmental and aging changes of Bak expression in the human brain.
Brain Res
783
:
167
–170.
Pfefferbaum A, Mathalon DH, Sullivan EV, Rawles JM, Zipursky RB, Lim KO (
1994
) A quantitative magnetic resonance imaging study of changes in brain morphology from infancy to late adulthood.
Arch Neurol
51
:
874
–887.
Rapoport JL, Giedd JN, Blumenthal J, Hamburger SD, Jeffries NO, Fernandez T, Nicolson R, Bedwell J, Lenane M, Zijdenbos A, Paus T, Evans A (
1999
) Progressive cortical change during adolescence in childhood-onset schizophrenia a longitudinal magnetic resonance imaging study.
Arch Gen Psychiat
56
:
649
–654.
Rasband W (1996) NIH IMAGE Manual. Bethesda, MD: National Institutes of Health.
Reiss AL, Abrams MT, Singer HS, Ross JL, Denckla Mbyte (
1996
) Brain development, gender and IQ in children a volumetric study.
Brain
119
:
1763
–1774.
Resnick SM, Goldszal AF, Davatzikos C, Golski S, Kraut MA, Metter EJ, Bryan NR, Zonderman AB (
2000
) One-year age changes in MRI Brain Volumes in older adults.
Cereb Cortex
10
:
464
–472.
Rosenberg DR, Keshavan MS, O'Hearn KM, Dick EL, Bagwell WW, Seymour AB, Montrose DM, Pierri JN, Birmaher B (
1997
) Frontostriatal measurement in treatment-naive children with obsessive–compulsive disorder.
Arch Gen Psychiat
54
:
824
–830.
Rosenberg DR, Sweeney JA, Gillen JS, Kim J, Varanelli MJ, O'Hearn KM, Erb PA, Davis D, Thulborn KR (
1997
) Magnetic resonance imaging of children without sedation: preparation with simulation.
J Am Acad Child Adolesc Psychiat
36
:
853
–859.
Seeman MV (
1997
) Psychopathology in women and men: focus on female hormones.
Am J Psychiat
154
:
1641
–1647.
Tanapat P, Hastings NB, Reeves AJ, Gould E (
1999
) Estrogen stimulates a transient increase in the number of new neurons in the dentate gyrus of the adult female rat.
J Neurosci
19
:
5792
–801.
Thompson PM, Giedd JN, Woods RP, MacDonald D, Evans AC, Toga AW (
2000
) Growth patterns in the developing brain detected by using continuum mechanical tensor maps.
Nature
404
:
190
–193.
Wechsler D (1974) Manual for the Wechsler Intelligence Scale for Children — Revised. New York, NY: The Psychological Corp.
Woolley CS, Jurgen Wenzel H, Schwartzkroin PA (
1996
) Estradiol increases the frequency of multiple synapse boutons in the hippocampal CA1 region pf the adult female rat.
J Comp Neurol
373
:
108
–117.
Xu J, Kobayashi S, Yamaguchi S, Iijima K, Okada K, Yamashita K (
2000
) Gender effects on age-related changes in brain structure.
Am J Neuroradiol
21
:
112
–118.
Yakovlev PI, LeCours AR (1967) The myelogenetic cycles of regional maturation of the brain. In: Regional development of the brain in early life (Minkowski A, ed.), pp. 3–70. Philadelphia, PA: Davis.
© Oxford University Press
© Oxford University Press
Topic:
- magnetic resonance imaging
- adolescent
- child
- sex characteristics
- brain
- gender
- myelination
- brain development
- white matter