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Neuroradiology |
1 From the Laboratory for Research on the Neuroscience of Autism, Children’s Hospital Research Center, 8110 La Jolla Shores Dr, Suite 201, La Jolla, CA 92037 (E.C., H.J.C., J.T., A.C., J.C., B.E., M.H., S.H.); the Department of Neurosciences, School of Medicine, University of California at San Diego, La Jolla (E.C., J.T.); and the Kaiser Permanente Hospital, San Diego, Calif (G.A.P.). Received April 5, 1999; revision requested June 7; final revision received December 7; accepted December 21. This work is supported by funds from NINDS (2-RO1-NS-19855) awarded to Eric Courchesne and NINDS (5RO1-NS-34155) award to Jeanne Townsend. Address correspondence to E.C. (e-mail: ecourchesne@ucsd.edu).
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ABSTRACT |
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 TOP ABSTRACT INTRODUCTIONMATERIALS AND METHODS RESULTS DISCUSSION REFERENCES |
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MATERIALS AND METHODS: Magnetic resonance (MR) images of 116 volunteers aged 19 months to 80 years were analyzed with semiautomated procedures validated by means of comparison with manual tracings. Volumes measured included intracranial space, whole brain, gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF). Results were compared with values from previous postmortem studies.
RESULTS: Whole brain and intracranial space grew by 25%–27% between early childhood (mean age, 26 months; age range, 19–33 months) and adolescence (mean age, 14 years; age range, 12–15 years); thereafter, whole-brain volume decreased such that volunteers (age range, 71–80 years) had volumes similar to those of young children. GM increased 13% from early to later (6–9 years) childhood. Thereafter, GM increased more slowly and reached a plateau in the 4th decade; it decreased by 13% in the oldest volunteers. The GM-WM ratio decreased exponentially from early childhood through the 4th decade; thereafter, it gradually declined. In vivo patterns of change in the intracranial space, whole brain, and GM-WM ratio agreed with published postmortem data.
CONCLUSION: MR images accurately depict normal patterns of age-related change in intracranial space, whole brain, GM, WM, and CSF. These quantitative MR imaging data can be used in research studies and clinical settings for the detection of abnormalities in fundamental neuroanatomic parameters.
Index terms: Aging • Brain, growth and development, 10.91 • Brain, MR, 10.121411, 10.12142 • Brain, volume, 10.91 • Magnetic resonance (MR), volume measurement, 10.121411, 10.12142
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INTRODUCTION |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODS RESULTS DISCUSSION REFERENCES |
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Even in the postmortem literature, there is a paucity of information on the parameters of brain development. For example, one study of postmortem specimens (19) provided measures of intracranial volume from birth through the age of 20 years, but for only males (see table 115 in the article by Blinkov and Glezer [19]), while another (20) provided such measures separately for both boys and girls, but for only the narrower age group of 2.5–8 years. In another study (21), intracranial volume was estimated from measures of the external height, width, and circumference of the skull. Since the routine method for measuring brain weight at autopsy includes the weight of CSF within the leptomeninges and the weight of parenchyma, in the postmortem literature, age-related changes in CSF may confound estimates of developmental changes in parenchyma. Furthermore, postmortem swelling and/or shrinkage due to fixation may introduce artifacts into measures of fresh or fixed postmortem brains (19).
Both postmortem and in vivo imaging studies (13,22–28) have demonstrated that, with advancing age, brain weight or volume, GM, and GM-WM ratios decrease and CSF volume increases. However, the timing of such age-related decreases and increases is uncertain. Transitions from growth-related increases to age-related decreases in several fundamental neuroanatomic parameters have been incompletely documented with in vivo MR imaging because developmental studies have commonly included samples of only children and adolescents or young adults, while aging studies have commonly included samples of only adults. Comparison of the neuroanatomic volume changes in developmental studies with those in aging studies would not help to resolve the timing of developmental and aging transitions because different imaging, measurement, and quantitative procedures were used. Also, while many aging studies included healthy volunteers, most studies of development included patients.
To our knowledge, there exist no published, quantitative, in vivo MR imaging studies of normal brain changes in the life span from early childhood (1 or 2 years of age) to late adulthood. We used currently available MR imaging procedures to accurately and objectively document the normal course of development and aging in whole brain, GM, WM, CSF, and intracranial volumes in male and female healthy volunteers aged 19 months through 80 years. Quantitation of these parameters in healthy volunteers allowed us to compare these values with published values in the postmortem literature.
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MATERIALS AND METHODS |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODS RESULTS DISCUSSION REFERENCES |
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Intelligence quotient (IQ) data were available for 86 of the 116 volunteers and revealed that IQ was comparable across the age range of the sample. Composite IQ scores obtained with the Stanford-Binet test (29) in 25 volunteers younger than 6 years was 95–141 (116.3 ± 11.0). Wechsler Intelligence Scale for Children-Revised or Wechsler Intelligence Scale for Children III full-scale IQ scores (30,31) for 33 volunteers aged 6 to 16 years were 90–133 (112.7 ± 11.0). Wechsler Adult Intelligence Scale-Revised or Wechsler Adult Intelligence Scale III full-scale IQ scores (32,33) for 36 volunteers older than 16 years were 90–137 (115.6 ± 14.0).
To screen for signs of memory loss, volunteers older than 50 years underwent the California Verbal Learning Test (34). All volunteers performed this test in the normal or well-above-normal range. (Standardized scores based on age norms, or T scores, for overall performance were 42–66 [55.1 ± 7.0].)
Volunteers were recruited by means of advertisements distributed in the community. On the basis of their responses to questionnaires administered at screening before MR imaging about medical, family, and educational history, volunteers showed no evidence of developmental, educational, medical, psychological, or psychiatric abnormalities or deficiencies. Prior to testing, the nature of the study and the procedures were explained to each volunteer, and informed consent was obtained. For each volunteer younger than 18 years, informed consent was obtained from a parent.
Imaging
MR images were acquired by using a 1.5-T Signa MR unit (GE Medical Systems, Milwaukee, Wis). To provide images for the computation of CSF, GM, and WM volumes, a dual spin-echo intermediate- and T2-weighted sequence was performed in the transverse plane (3,000/30 and 80 [repetition time msec/echo time msec], one signal acquired, 20-cm field of view, 256 x 256 matrix, 3-mm interleaved [no gaps]). No medication or sedation was used during the imaging procedure. To help ensure that younger children remained still for imaging, scanning was performed during natural sleep. Occasionally, the staff would pacify younger children who were not sleeping by singing to them during the MR imaging examination. The MR imaging data were transferred in digital format to workstations (Silicon Graphics; Mountain View, Calif) for analysis. All images were coded so that the operators (H.J.C., A.C., J.C.) were blinded to each volunteer’s identity, sex, and age.
Automated Correction for Signal Falloff
Images were preprocessed (by B.E.) by using SEGMENT (Egaas B and Courchesne E, San Diego, Calif) to correct for decreased signal intensity toward the inferior sections by using a fully automated algorithm. In this algorithm, pixels in the lowest signal intensity range (ie, bone of the skull base, air in the sinuses) were automatically excluded prior to calculation of the difference in the mean signal intensity of the remaining pixels between pairs of consecutive section locations. Then, on the basis of a higher-order polynomial best fit of those difference values, a separate gain coefficient for each section was derived and applied to each section so that the mean signal intensity of adjacent sections differed by less than 1%. The gain coefficient was constant within a section, and the same coefficient was applied to corresponding intermediate- and T2-weighted images. Next, semiautomated and fully automated steps were used to remove skull and extracranial structures and to classify GM, WM, and CSF as follows.
Semiautomated Removal of Skull and Extracranial Structures
In all volunteers, skull and extracranial structures were removed from the T2-weighted images with a semiautomated procedure that used a computer algorithm called "brain removal" (S.H.) and with decision making by experienced neuroanatomists (H.J.C., A.C., J.C.) in a section-by-section process. One of the operators set the initial threshold level on each T2-weighted image to separate the higher signal intensity of the brain and CSF from the lower signal intensity of the skull and air. Next, manual tracing was performed to separate contiguous nonbrain pixels from brain or CSF pixels with similar signal intensity values. The remaining intracranial pixels (ie, all brain and CSF pixels) were selected by using an automated region-growing algorithm that retained the original signal intensity values of all intracranial pixels (Fig 1a). The final T2-weighted images that depicted only brain and CSF were used as a mask on the original intermediate-weighted images to automatically obtain the corresponding intermediate-weighted images of only brain and CSF (Fig 1b). The entire process was completed in less than 45 minutes per brain.
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Anatomically, then, whole brain volume was defined as all brainstem, infundibular and pituitary, cerebellar, subcortical, and cerebral parenchyma. Total intracranial CSF volume was defined as all CSF in ventricular and subarachnoid spaces, and intracranial space volume was defined as the sum of whole brain and CSF volumes.
Automatic Classification of GM, WM, and CSF
In the next step of the SEGMENT process with each brain data set, all pixels (including all skull and extracranial pixels) were used to form a global histogram in a intermediate-weighted (y axis) versus T2-weighted (x axis) feature space (Fig 1c). Each cluster (Fig 1c) was mathematically mapped as a joint Gaussian distribution in this intermediate- and T2-weighted feature space; the location and orientation of each cluster may be visualized as an ellipse that has major axis points oriented toward the origin (where the signal intensity values of intermediate- and T2-weighted images both equal 0). By using a maximum likelihood criteria algorithm based on previous approaches (35), all pixels were then automatically classified (by B.E.) as parenchyma (WM and GM clusters in Fig 1c), CSF (CSF cluster in Fig 1c), CSF partially volumed with skull (cluster not shown), or other (ie, all remaining nonparenchymal and non-CSF pixels [eg, nonbrain clusters in Fig 1c]).
All parenchymal pixels were then automatically separated into GM and WM pixels (by B.E.) with a three-dimensional local-contrast algorithm SEGMENT. With this algorithm, the local threshold for GM and WM pixels was computed from pixel statistics within a 2 x 2 x 2-cm cube surrounding the pixel being classified. (The cube was 29 pixels x 29 pixels x 7 sections, or 5,887 surrounding voxels.) The use of this local contrast makes segmentation relatively insensitive to the signal intensity inhomogeneities intrinsic to imaging that plague simpler methods of segmentation, such as uniform thresholding over an entire section. In particular, this method successfully handles the signal intensity reductions often found at the occipital pole and lateral extent of the temporal lobes.
Pixels that the three-dimensional local contrast algorithm determined to be mostly WM were classified as WM, and pixels determined to be mostly GM were classified as GM. This classification preserved a sharp outline between the cortical ribbon and adjacent axonal material (Fig 1d). The approach is analogous to the previous approaches that use feature space in the semiautomated segmentation of pixels on images with nearly identical intermediate- and T2-weighted protocols (28,36); the principal difference is that the present algorithm is fully automated.
Validation of the Automated Classification Algorithm
The automated classification was validated by using randomly selected images of eight brains from those of the study group without any foreknowledge of image quality; they included images in five male and three female young children and adults. In each brain, all GM, WM, and CSF spaces within the right hemibrain were manually traced (by H.J.C.) from the T2- and intermediate-weighted images at the following three section levels—the centrum semiovale, the thalamus and basal ganglia, and the pons (see Fig 1a, 1b for tracings in one section of one brain). The area was calculated for pixels designated as GM, WM, or CSF within each manual tracing of each section. Manually traced and automatically segmented tissue classifications were compared by means of qualitative visual examination and quantitative statistical analysis.
Visual examination was performed (by E.C., G.A.P., H.J.C., and B.E.) by overlaying the results of the manual tracings onto the automatically classified images (Fig 1d). Correspondence between automated and manual approaches was evaluated in the cerebrum, cerebellum, globus pallidus, lateral thalamus, substantia nigra, and red nucleus. Since the combined volume of these subcortical structures accounts for approximately 1% of total brain volume (12), a modest measurement error in these structures will result in an error of much less than 1% in the whole brain, GM, and WM volumes calculated in our study.
In an additional validation step, the experienced neuroanatomist (H.J.C.) manually traced cortical GM on 20 sections in a ninth brain using the intermediate- and T2-weighted images; measures from these manual tracings and those of the automated classification were statistically compared.
That these fully automated (SEGMENT) and semiautomated steps resulted in valid brain measures is further supported by the results described later that showed comparable age-related changes in intracranial space volume in MR imaging and postmortem studies, comparable changes in the mean intracranial space volume in adolescents and adults, comparable estimates of whole brain weight, and comparable age-related changes in the GM-WM ratio.
Calculation of Intracranial, Whole Brain, CSF, GM, and WM Volumes
Each two-dimensional pixel on the images represents a three-dimensional volume (voxel), and the intracranial space volume was determined by summing the volume of all voxels designated as GM, WM, and CSF and half of the volume of all voxels designated as CSF partially volumed with skull. Whole brain volume was determined in a similar fashion by using only GM and WM voxels. Total CSF volume was constructed from the full volume of CSF voxels and half of the volume of all voxels designated as CSF partially volumed with skull.
Estimation of Brain Weight at MR Imaging
At autopsy, it is common practice to weigh brains with the leptomeninges and ventricles intact, trapping CSF within and around the brain. Therefore, to compare brain size at in vivo MR imaging with brain size in postmortem studies, it is necessary to obtain an estimation of brain weights from MR imaging-derived measures that are comparable to normative brain weights published in the postmortem literature. This estimation can be performed by converting MR imaging–derived volumes of parenchyma (ie, whole brain) and CSF into grams per milliliter and then by summing these values as follows: brain weight in grams = (whole brain volume in milliliters x 1.0365 g/mL) + (CSF volume in milliliters x 1.00 g/mL) (37,38).
Statistical Analyses of Volume Measurement Data
One author (J.T.) ensured the use of the appropriate statistical tests. Statistical analysis was performed (by J.T.) with the Biomedical Statistical Software Package, or BMDP (SPSS; Berkeley, Calif) (39). Statistical probabilities reported for regression analyses were tests of significance for the orthogonal polynomial regression coefficients.
Comparison of the change with age in our in vivo brain volume estimates and the brain weight measures obtained from published postmortem data involved the use of standard regression analyses with effect coding in an evaluation of both linear and quadratic differences with age between the in vivo MR imaging data and postmortem data (40). Independent variables in these analyses were study type (postmortem or in vivo MR imaging), linear and quadratic age factors, interactions of study type, and linear and quadratic age. Measures from the 11 published postmortem studies (see tables 111, 113, 115, 117, and 119 in reference 19) (41–46) cited here for comparison were available as only means (for various age groups) and not as individual data points. In our analyses, we assumed that these means were robust and that they represented the data of an individual at the given age. A P value of .05 indicated a statistically significant difference.
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RESULTS |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODS RESULTS DISCUSSION REFERENCES |
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Whole Brain Volume
Age-related change in whole brain volume is shown in Figure 2b. Whole brain volume grew exponentially by 25% between early childhood (19–33 months) and adolescence (12–15 years), when the developmental maximum volume was reached (linear component, R = 0.53, R2 = 0.28, t58 = 4.72, P < .001; quadratic component, change in R2 = 0.06, t57 = 2.22, P < .05) (Fig 2b). In the age range of 16–80 years, whole brain volume slowly but steadily declined so that in those aged 71–80 years, it had dropped by 26% and was smaller than that of healthy 2- or 3-year-old children (linear component, R = 0.57, R2 = 0.32, t54 = 5.09, P < .001; quadratic component, change in R2 = 0.06, t53 = 2.28, P < .05). Across all ages, whole brain volumes were about 12% smaller in female volunteers than in male volunteers (1,137.8 mL ± 109 vs 1,286.4 mL ± 133, t114 = 5.92, P < .001).
Figure 2d depicts a graph of age-related changes in postmortem brain weight from 11 studies (see tables 111, 113, 115, 117, and 119 in reference 19) (41–46), age-related changes in our in vivo whole brain volumes (brain only), and our estimated in vivo whole brain weights (estimated brain weight + CSF weight; see Materials and Methods). For each study, weight or volume at each age was plotted as a function of a percentage of the maximum weight or volume for that study (eg, for our study, the maximum was achieved at 12–15 years of age).
The graph and statistical analyses showed no difference in change with age between postmortem data and our in vivo estimated whole brain weight. Regression analysis with effect coding for study type (see Materials and Methods, Statistical Analyses of Volume Measurement Data) yielded a model in which there were significant linear and quadratic components in the association of age with brain weight (R = 0.40, F2,224 = 21.85, P < .001, linear coefficient t = 5.44, P < .001; quadratic coefficient t = 6.56, P < .001 [Fig 2d]). There was no association between study type (postmortem weight, in vivo estimated weight) and brain weight, and there were no significant interactions between study type and age. The correlations of brain weight with study type was 0.16, and the interaction of study type with linear age and quadratic age was 0.003 and -0.27, respectively. A regression model with study type, linear age with study type interaction, and quadratic age with study type interaction as independent variables showed no relationship between these factors and brain weight (R2 = 0.005).
The graph and statistical analyses showed similar changes in growth but not aging in the postmortem data and in our in vivo brain volume. A comparison of postmortem brain weight and whole brain volume (only GM and WM) at MR imaging showed significant differences in the rate of change with age as a function of the study type (postmortem weight, in vivo whole brain volume). This difference was due to the increase in CSF and the complementary decrease in whole brain volume with aging seen in the MR imaging measures but not in the postmortem measures of weight, which included the weight of CSF. By using the same set of independent variables as those used in the previous analysis, the model of best fit selected study type, the quadratic age component, and the interaction between study type and linear age (R = 0.56, F3,223 = 34.53, P < .001; quadratic age coefficient, t = 8.25, P < .001; study-type coefficient, t = 4.45, P < .001; study-type and linear-age interaction coefficient, t = 6.28, P < .001) (Fig 2d).
In addition, in the present study, the mean estimated brain weight from in vivo MR imaging data was 1,527 g ± 120 for male adolescents and adults (age range, 16–43 years) and 1,363 g ± 99 for female adolescents and adults (age range, 17–49 years). These MR imaging–derived estimates of brain weight were about 4% greater than the mean brain weights reported for male and female participants in six different postmortem normative studies involving more than 6,000 participants aged 17–44 years (table 118 in reference 19) (41–43,47,48). In those six studies, the median of the mean brain weight was 1,460 g ± 113 in male participants (range of study means, 1,443–1,504 g) and 1,305 g ± 106 in female participants (range of study means, 1,288–1,398 g). The small 4% difference between our in vivo estimated brain weight and the postmortem weight may be due to sampling differences or to secular increases in brain size in the past several decades. Alternatively, it may be that our slightly greater estimate in the living brain reflects the fact that, in living brain, 2%–3% of its volume is blood most of which is in GM capillaries (19).
GM Volume
Age-related change in GM is shown in Figure 3a. GM increased 13% from early childhood (19–33 months) to later childhood (6–9 years) (linear component, R = 0.37, R2 = 0.14, t41 = 2.64, P < .05). Thereafter, it decreased linearly by approximately 5% per decade throughout life (linear component, R = 0.75, R2 = 0.57, t71 = 9.63, P < .001) (Fig 3a). The percentage of GM contributing to intracranial space volume was not different in male and female volunteers (female volunteers, 58.5% ± 0.08%; male volunteers, 59.9% ± 0.07%).
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GM-WM Ratio
As shown in Figure 3c, the GM-WM ratio decreased rapidly from early childhood (19–33 months) to later adulthood (about 50–60 years). Thereafter, it slowly but steadily declined (linear component, R = 0.69, R2 = 0.47, t114 = 10.10, P < .001; quadratic component, change in R2 = 0.24, t113 = 9.63, P < .001). GM-WM ratios were not different in male and female volunteers (female volunteers, 2.11 ± 0.6; male volunteers, 2.12 ± 0.5).
The present MR imaging–based age-related decline in the GM-WM ratio is generally consistent with postmortem study–derived age-related differences in this ratio, as originally reported nearly a century ago (49,50), and with the more contemporary postmortem data (25) and recent MR imaging–derived values (Fig 3d) (27,51,52).
Total Intracranial CSF Volume
Total ventricular and extracerebral CSF volume increased during the life span (Fig 4a). The total volume of CSF (ventricular and subarachnoid) more than doubled between early childhood (19–33 months) (approximately 96 mL) and middle adulthood (40–55 years) (approximately 250 mL). This total CSF volume increased linearly with age in the entire sample of both male and female volunteers (male volunteers, R = 0.84, R2 = 0.71, t77 = 13.84, P < .001; female volunteers, R = 0.88, R2 = 0.78, t35 = 11.19, P < .001).
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Correlation Between Manual and Automatic Classification Methods
Statistical analyses were used to confirm that our automated method of measurement provided classifications with high fidelity to the results of the manual tracings performed on intermediate- and T2-weighted images of eight randomly selected volunteers. Correlations between manual and automatic classification of three different section locations within the right hemibrain in these eight brains were 0.982 for GM, 0.961 for WM, and 0.887 for CSF (Fig 1e).
Visual examination conducted by overlaying the results of the manual tracings on the images of the eight brains onto the automatically classified images (eg, Fig 1d) showed that correspondence between the automated and manual approaches was best and equally good in the cerebrum and cerebellum and was worst in the globus pallidus, lateral thalamus, substantia nigra, and red nucleus.
We also studied the correlation between the manual tracings and the automatic classification of the cortical GM (alone) on 20 intermediate- and T2-weighted images from the ninth randomly selected brain. Measures from those manual tracings and from the automated classification correlated to a level of more than 99%.
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DISCUSSION |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODS RESULTS DISCUSSION REFERENCES |
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This pattern of life-span change is consistent with findings of previous studies of either development or aging.
Studies of development.—Postmortem study findings concur that the brain of neonates weighs 370 g on average and that its weight increases nearly threefold by 2 years of age (19,42,44–46). The postmortem data (42) show that, from infancy to adolescence, brain weight increases by 29%, an amount similar to the 25% increase in brain volume noted in our study (Fig 2d). In postmortem studies (19,46) that focused on the life span from birth to adolescence or young adulthood, brain weight increased by about 32% from early childhood to adolescence. As with brain growth (Fig 2d), growth of intracranial volume is also similar between postmortem studies and our in vivo study (Figs 2c).
To our knowledge, no previous in vivo MR imaging study has quantified growth in whole brain and intracranial volumes in healthy young children; only groups of clinically-referred pediatric patients who are termed "neurologically normal" or "radiologically normal" have been studied. For example, in one group of such patients, there was "a rapid increase by 2 years of age, and thereafter a gradual increase until about 12 years" (10). Their growth curves show that, from early childhood to adolescence, cerebral volumes grew exponentially by about 22%, which is a magnitude of change comparable with that of the whole brain volume (ie, 25%) in the present study. In another study (27), pediatric patients had roughly a 30% increase in intracranial volume from infancy and early childhood to about 10 years of age, when maximum volumes were reached. In rare studies (53,54), cerebral volumes were measured in healthy children and adolescents. Neither study reported statistically significant age-related changes in cerebral volume, possibly because few young volunteers were included.
Studies of aging.—A large number of study findings agree that brain size decreases with advancing age (22), but the magnitude of decrease differs substantially between postmortem brain weight data on the one hand and in vivo whole brain volumes on the other (Fig 2d). According to findings of postmortem studies, brain weight decreases by only about 9% in males and females alike from a maximum in adolescence to late adulthood (71–80 years) (19,41–43,48).
When we converted our brain and CSF volumes to brain weight (autopsy brain weight in grams = brain volume in milliliter x 1.0365 g/mL + CSF volume in milliliter x 1.00 g/mL), our results showed a similar small decrease (9%) in weight from adolescence to late adulthood (71–80 years; compare open and black symbols in Fig 2d). However, this is a far smaller decrease than the 26% decrease found in brain (GM and WM) volumes in our study (compare open and red symbols in Fig 2d). The discrepancy between age-related changes based on brain weight determined at autopsy and those based on in vivo brain volume is likely caused by the inclusion of the weight of CSF trapped in the ventricles and leptomeninges at autopsy as brain weight. In healthy older individuals, CSF volume and its weight can be considerable. As noted previously for the present study, CSF accounts for a small percentage (7%–12%) of the total intracranial volume in a healthy young person (and, therefore, would account for a small percent of brain weight in a standard measurement at autopsy); however, in 71–80-year-old adults, CSF in the ventricles and leptomeninges can account for 16%–25% of the intracranial volume (present study) (22,24,26,28).
Whole Brain GM Volume
In vivo MR imaging data show that cerebral GM volume increases linearly from 29 to 41 weeks after conception (55), continues to increase to 4–6 years of age (27), and decreases thereafter (27). Our in vivo study revealed a similar age-dependent profile for whole brain GM (compare our Fig 3a to figure 3a in the article by Pfefferbaum et al [27]). GM volume increased 13% from early childhood (mean age, 26 months) to later childhood (6–9 years), when maximum volume was reached (which is several years before peak whole brain volume is reached). From late childhood, GM decreased linearly by about 5% per decade throughout life. One recent study (13) revealed that most sampled regions showed linear age-related decreases in cerebral GM from 20 to 80 years; prefrontal and superior parietal cortex, respectively, decreased by 4.9% and 4.3% per decade, but some cortical areas, such as the anterior cingulate and primary motor cortex, decreased little.
The particular biologic mechanisms underlying this striking age-dependent profile of GM volume increase and decrease are not known with certainty. There are no comparable age-related increases and decreases in the number of neurons in the healthy brain that can account for these changes in GM volume. However, for the period from 29 weeks after conception through 6–9 years of age (during which the combined in vivo MR imaging data from our study and others [27,55] demonstrate increasing GM volume), prior postmortem studies report corresponding increases in the size of neuronal cells (19), increases in the extent of neuronal arbors (56), and increases in the number of synapses (57) and neurons (58).
The rapid growth of GM (including each of its neuronal components and the enhanced functional responses that characterize the period from 19 months to 9 years of age) is followed by a period of elimination of cerebral synapses (57) slowed the growth in cell and arbor size slows (19,56) and decreases in cerebral metabolic and neurophysiologic indices (59–61). While these changes might be related to the onset of the decline in GM volume (which follows the achievement of peak volumes at about 6–9 years of age and which might also continue to contribute to the decline in late childhood and adolescence), they are largely completed by adolescence, and, therefore, they cannot explain the continued linear decline throughout early, middle, and late adulthood. Conversely, the various hypotheses that have been advanced to explain why GM declines during aging (eg, age-related atrophy in subcortical monoaminergic structures that supply cortical neurons and decline in the ability to renew synapses and repair of neurons) cannot explain the GM decline from late childhood through adolescence. It seems unlikely, therefore, that a single factor can account for the decline in GM that lasts nearly throughout the life-span; perhaps at different ages different factors operate to cause GM volume changes.
Whole Brain WM Volume
Prior in vivo MR imaging data show a sudden increase in whole-brain myelinated WM between 29 and 41 weeks after conception (55). From infancy through early adulthood, in vivo studies of cerebral WM reveal large increases in volume (10,27), as do studies of older children and adolescents (54). In our study, whole brain WM volume increased in a manner parallel to these developmental changes by about 74% from early childhood to adolescence; thereafter, it increased at a slower rate and finally reached a plateau by the 4th decade of life (Fig 3b).
We detected a decrease in WM volume of 13% in the oldest volunteers (aged 71–80 years). Others (13) found age-related volume decreases to be slight or not detectable in several cortical regions, and only prefrontal and superior parietal regions had statistically significant declines in volume with advancing age.
GM-WM Ratio
The opposing age-related patterns of volume change in GM and WM combine to make a striking curve of change in the GM-WM ratio throughout the life span (Fig 3c, 3d). In our study and in that of others (27), the steady rise in WM and decline in GM led to a sharply declining, curvilinear change in the GM-WM ratio from early childhood through adolescence. Apparently, beginning at about 20 years of age, the much slower rate of increase in WM combined with the steady decrease in GM substantially slowed the rate of decline in the GM-WM ratio in our study population. This life-span pattern of change is consistent with postmortem (49,50) and in vivo MR imaging data (27,51,52) that show that the GM-WM ratio in the very young brain is far larger than that of the adult brain and that, from 20 to 80 years of age, this ratio changes only slightly (25).
CSF Volumes
In our volunteer population, the absolute volume of ventricular and extracerebral CSF increased substantially and nearly linearly throughout the life span (from approximately 96 mL in toddlers to approximately 350 mL in 80-year-old adults) (Fig 4a). Therefore, increases in the absolute volume of intracranial CSF are a phenomenon of not only aging but the entire life span.
Nevertheless, the percentage of CSF that occupied intracranial space (Fig 4b) remained nearly constant at 7%–9% during the entire period of rapid intracranial and brain growth from early childhood to early adolescence. Thereafter, with little further change in intracranial space, but with decreasing brain volume with advancing age, the percentage of CSF that occupied intracranial space, as well as the absolute volume of CSF, increased from 14% during young and middle adulthood to 25% in our healthy older adults. It appears, therefore, that from adolescence through late adulthood, the percentage of CSF that occupies intracranial space gradually increases.
There are no developmental, postmortem, or in vivo MR imaging studies of changes in total intracranial CSF volumes. However, among a large number of in vivo studies of aging (22,26,27), there is uniform agreement that intracranial CSF increases with advancing age. For instance, a recent study (24) reported that CSF that occupied intracranial space increased from 12% to 17% between 26 and 70 years of age.
The absolute amounts of intracranial CSF reported in older adults varies somewhat from study to study (typically from 250 to 400 mL) most likely because different studies used different definitions of intracranial volume or CSF spaces. Our study included all brain, CSF, and intracranial space from the vertex to the level of the bottom of the medulla, and, so, we gave a "true" total intracranial CSF volume that was larger than, for instance, the volume calculated by others (22) who excluded CSF in the posterior fossa.
Cross-sectional and Longitudinal Study Designs
Cohort effects are unavoidable in any cross-sectional study of age-related changes during development and aging. Longitudinal studies are also vulnerable to cohort effects; for instance, longitudinal changes observed in a single cohort may not be representative of the population at large. Nonetheless, longitudinal MR imaging studies can have greater statistical power than that of cross-sectional studies in the detection of small effects, and the power of the longitudinal MR imaging design has been convincingly demonstrated in healthy individuals (62) and in those with different disorders (63,64).
However, the most powerful design for studying development and aging changes and for ruling out potential cohort effects is the cross-sequential design that combines cross-sectional and longitudinal procedures. (An example of such a longitudinal MR imaging study of children and adolescents if that of Giedd et al [65], which was published while the present article was in press.) Quantitative MR imaging is ideally suited for this, and the present article represents the cross-sectional phase of our cross-sequential design; our group has nearly completed data collection in a long-term, longitudinal MR imaging phase; results from this combined, cross-sequential, quantitative MR imaging study will be reported in the future.
Clinical and Research Utility of Quantitative MR imaging
Our quantitative MR imaging procedures yielded indices of normal brain development and aging throughout the life span that faithfully followed the age-related time courses predicted from quantitative postmortem procedures. Our in vivo normative life-span MR imaging data may be useful in clinical settings and in research studies for detecting and monitoring pathologic alterations in fundamental neuroanatomic indices in patients with suspected congenital, developmental, or acquired disorders. For example, using the normative values and quantitative MR imaging procedures in the present study, we detected macrencephaly in young children suspected of being autistic (Courchesne E, unpublished data, 1991–1996). In spinocerebellar ataxia type 2 (66), a statistically significant correlation exists between supratentorial atrophy and disease duration (whereas the degree of infratentorial atrophy does not correlate with disease duration). The combination of normative age-related changes with results from serial quantitative MR imaging of intracranial compartments in spinocerebellar ataxia type 2 may help in the detection of the progressive steps of this disease. Moreover, individual research studies of a number of disorders have shown that volumetric MR imaging analyses of basic brain parameters can be used to distinguish disorders from normal states and to distinguish diseases from each other (67,68), but the routine clinical use of such information via quantitative MR imaging assays has remained limited.
Our study adds evidence indicating that quantitative MR imaging assays of several fundamental neuroanatomic indices in a routine clinical setting is feasible. Until recently, quantitative MR imaging examinations of the neuroanatomy were not a practical option in routine clinical neuroradiologic examinations primarily because they were (a) dependent on specialized imaging protocols, (b) labor intensive, (c) dependent on a high level of detailed anatomic knowledge and manual tracing, (d) dependent on specialized computer hardware, or (e) based on homegrown software that required specialized computer or biomedical knowledge to operate.
Our study, along with a number of other recent studies (26,28,36), involved the use of routine intermediate- and T2-weighted imaging protocols and semiautomated approaches that circumvent these obstacles. For instance, in our study, classification of GM, WM, and CSF pixels was accomplished by using a fully automated algorithm that (a) was based on known and tested principles (28,35,36); (b) was validated against neuroanatomic tracings performed by an experienced neuroanatomist on images in young children, adolescents, and adults; (c) provided results with measurement errors of only 1%–2% or less; and (d) clinical MR imaging technologists could easily operate.
Moreover, this algorithm can be used on computers that are commonly available at clinical imaging facilities. The primary drawback with our algorithm is a single semiautomated "skull removal" step that involves manual tracing and that requires about 45 minutes to complete. However, adaptation of this step to meet time and personnel needs in a clinical setting is practical, and is, in fact, fast (approximately 5 minutes), although easy-to-use, less precise methods that implement that single step are already commercially available. With current procedures, therefore, reliable and accurate normative brain indices, such as those of the present report, can be used in any clinical facility as quantitative clinical standards against which possible congenital, acquired, or treatment-related deviations from normal development across the life span can be judged.
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ACKNOWLEDGMENTS |
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FOOTNOTES |
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Author contributions: Guarantor of integrity of entire study, E.C.; study concepts, E.C., G.A.P.; study design, E.C.; definition of intellectual content, E.C.; literature research, E.C., H.J.C.; clinical studies, B.E., S.H.; data acquisition, J.T., J.C., E.C., A.C.; data analysis, H.J.C., A.C., B.E.; statistical analysis, J.T., M.H.; manuscript preparation, E.C., H.J.C., G.A.P., J.T.; manuscript editing, E.C., G.A.P., H.J.C.; manuscript review, E.C., G.A.P.
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODS RESULTS DISCUSSION REFERENCES |
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), Jaeger (
), and Miller et al (
= data in males, 
= data in females), Pfefferbaum et al (
), and Narayana and Borthakur (
).
