HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS		QUICK SEARCH:   [advanced] Author: Keyword(s): Year:  Vol:  Page:

This Article Abstract Full Text Full Text (PDF) Submit a response Alert me when this article is cited Alert me when eLetters are posted Alert me if a correction is posted Citation Map Services Email this article to a friend Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via HighWire Citing Articles via Google Scholar Google Scholar Articles by Courchesne, E. Articles by Press, G. A. Search for Related Content PubMed PubMed Citation Articles by Courchesne, E. Articles by Press, G. A.

Normal Brain Development and Aging: Quantitative Analysis at in Vivo MR Imaging in Healthy Volunteers¹

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).

View larger version (174K): [in a new window]   Figure 1a. Images and graphs illustrate how SEGMENT, a fully automated algorithm, uses (a) T2- and (b) intermediate-weighted images to plot all pixels as (c) a global histogram in a T2-weighted (x axis) versus intermediate-weighted (PD; y axis) feature space. SEGMENT then uses an algorithm with maximum likelihood criteria to classify pixel clusters as brain, CSF, CSF partially volumed with skull (cluster not shown in figure), or other (nonbrain). Next, for all pixels classified as brain, SEGMENT uses a three-dimensional local-contrast algorithm to separate GM and WM pixels, which creates (d) final GM-, WM-, and CSF-segmented images. For qualitative comparison, manual tracings of T2- and intermediate-weighted pairs of images (eg, see tracings in a and b) were overlaid onto SEGMENT-created images (tracing in d). (e) Validation of SEGMENT was accomplished by means of statistical comparison with results from manual tracing; analyses showed that GM and WM measures obtained by using SEGMENT and manual-tracing methods correlated to 98.2% and 96.1%, respectively.

View larger version (180K): [in a new window]   Figure 1b. Images and graphs illustrate how SEGMENT, a fully automated algorithm, uses (a) T2- and (b) intermediate-weighted images to plot all pixels as (c) a global histogram in a T2-weighted (x axis) versus intermediate-weighted (PD; y axis) feature space. SEGMENT then uses an algorithm with maximum likelihood criteria to classify pixel clusters as brain, CSF, CSF partially volumed with skull (cluster not shown in figure), or other (nonbrain). Next, for all pixels classified as brain, SEGMENT uses a three-dimensional local-contrast algorithm to separate GM and WM pixels, which creates (d) final GM-, WM-, and CSF-segmented images. For qualitative comparison, manual tracings of T2- and intermediate-weighted pairs of images (eg, see tracings in a and b) were overlaid onto SEGMENT-created images (tracing in d). (e) Validation of SEGMENT was accomplished by means of statistical comparison with results from manual tracing; analyses showed that GM and WM measures obtained by using SEGMENT and manual-tracing methods correlated to 98.2% and 96.1%, respectively.

View larger version (36K): [in a new window]   Figure 1c. Images and graphs illustrate how SEGMENT, a fully automated algorithm, uses (a) T2- and (b) intermediate-weighted images to plot all pixels as (c) a global histogram in a T2-weighted (x axis) versus intermediate-weighted (PD; y axis) feature space. SEGMENT then uses an algorithm with maximum likelihood criteria to classify pixel clusters as brain, CSF, CSF partially volumed with skull (cluster not shown in figure), or other (nonbrain). Next, for all pixels classified as brain, SEGMENT uses a three-dimensional local-contrast algorithm to separate GM and WM pixels, which creates (d) final GM-, WM-, and CSF-segmented images. For qualitative comparison, manual tracings of T2- and intermediate-weighted pairs of images (eg, see tracings in a and b) were overlaid onto SEGMENT-created images (tracing in d). (e) Validation of SEGMENT was accomplished by means of statistical comparison with results from manual tracing; analyses showed that GM and WM measures obtained by using SEGMENT and manual-tracing methods correlated to 98.2% and 96.1%, respectively.

View larger version (166K): [in a new window]   Figure 1d. Images and graphs illustrate how SEGMENT, a fully automated algorithm, uses (a) T2- and (b) intermediate-weighted images to plot all pixels as (c) a global histogram in a T2-weighted (x axis) versus intermediate-weighted (PD; y axis) feature space. SEGMENT then uses an algorithm with maximum likelihood criteria to classify pixel clusters as brain, CSF, CSF partially volumed with skull (cluster not shown in figure), or other (nonbrain). Next, for all pixels classified as brain, SEGMENT uses a three-dimensional local-contrast algorithm to separate GM and WM pixels, which creates (d) final GM-, WM-, and CSF-segmented images. For qualitative comparison, manual tracings of T2- and intermediate-weighted pairs of images (eg, see tracings in a and b) were overlaid onto SEGMENT-created images (tracing in d). (e) Validation of SEGMENT was accomplished by means of statistical comparison with results from manual tracing; analyses showed that GM and WM measures obtained by using SEGMENT and manual-tracing methods correlated to 98.2% and 96.1%, respectively.

View larger version (31K): [in a new window]   Figure 1e. Images and graphs illustrate how SEGMENT, a fully automated algorithm, uses (a) T2- and (b) intermediate-weighted images to plot all pixels as (c) a global histogram in a T2-weighted (x axis) versus intermediate-weighted (PD; y axis) feature space. SEGMENT then uses an algorithm with maximum likelihood criteria to classify pixel clusters as brain, CSF, CSF partially volumed with skull (cluster not shown in figure), or other (nonbrain). Next, for all pixels classified as brain, SEGMENT uses a three-dimensional local-contrast algorithm to separate GM and WM pixels, which creates (d) final GM-, WM-, and CSF-segmented images. For qualitative comparison, manual tracings of T2- and intermediate-weighted pairs of images (eg, see tracings in a and b) were overlaid onto SEGMENT-created images (tracing in d). (e) Validation of SEGMENT was accomplished by means of statistical comparison with results from manual tracing; analyses showed that GM and WM measures obtained by using SEGMENT and manual-tracing methods correlated to 98.2% and 96.1%, respectively.

View larger version (28K): [in a new window]   Figure 2a. Graphs depict age-related volume changes in (a) intracranial space and (b) whole brain in our 116 healthy volunteers. Volume intercepts at birth are based on quantitative MR imaging findings of Huppi et al (55). (c) Graph shows that our in vivo age-related intracranial space growth rates are similar to those of previous postmortem studies (table 115 in the study by Blinkov and Glezer [19]; Lichtenberg [20]). (d) Graph shows similar growth rates in our in vivo whole brain volume (red symbols), estimated in vivo whole brain weight (black symbols; estimated whole brain weight = brain weight + CSF weight), and brain weight (open symbols) from 11 previously published postmortem studies (tables 111, 113, 115, 117, 119 in reference 19) (41-46). In the present study and in each published study, volume or weight at each age is plotted as a function of a percentage of the maximum volume or weight for that study. Postmortem weight includes CSF, but our in vivo MR imaging whole brain volume does not. Therefore, during development, CSF weight makes a small contribution to the reported brain weight, but, with aging, its weight makes a successively greater contribution. (On x axes, age scales change after 20-year point.)

View larger version (29K): [in a new window]   Figure 2b. Graphs depict age-related volume changes in (a) intracranial space and (b) whole brain in our 116 healthy volunteers. Volume intercepts at birth are based on quantitative MR imaging findings of Huppi et al (55). (c) Graph shows that our in vivo age-related intracranial space growth rates are similar to those of previous postmortem studies (table 115 in the study by Blinkov and Glezer [19]; Lichtenberg [20]). (d) Graph shows similar growth rates in our in vivo whole brain volume (red symbols), estimated in vivo whole brain weight (black symbols; estimated whole brain weight = brain weight + CSF weight), and brain weight (open symbols) from 11 previously published postmortem studies (tables 111, 113, 115, 117, 119 in reference 19) (41-46). In the present study and in each published study, volume or weight at each age is plotted as a function of a percentage of the maximum volume or weight for that study. Postmortem weight includes CSF, but our in vivo MR imaging whole brain volume does not. Therefore, during development, CSF weight makes a small contribution to the reported brain weight, but, with aging, its weight makes a successively greater contribution. (On x axes, age scales change after 20-year point.)

View larger version (32K): [in a new window]   Figure 2c. Graphs depict age-related volume changes in (a) intracranial space and (b) whole brain in our 116 healthy volunteers. Volume intercepts at birth are based on quantitative MR imaging findings of Huppi et al (55). (c) Graph shows that our in vivo age-related intracranial space growth rates are similar to those of previous postmortem studies (table 115 in the study by Blinkov and Glezer [19]; Lichtenberg [20]). (d) Graph shows similar growth rates in our in vivo whole brain volume (red symbols), estimated in vivo whole brain weight (black symbols; estimated whole brain weight = brain weight + CSF weight), and brain weight (open symbols) from 11 previously published postmortem studies (tables 111, 113, 115, 117, 119 in reference 19) (41-46). In the present study and in each published study, volume or weight at each age is plotted as a function of a percentage of the maximum volume or weight for that study. Postmortem weight includes CSF, but our in vivo MR imaging whole brain volume does not. Therefore, during development, CSF weight makes a small contribution to the reported brain weight, but, with aging, its weight makes a successively greater contribution. (On x axes, age scales change after 20-year point.)

View larger version (32K): [in a new window]   Figure 2d. Graphs depict age-related volume changes in (a) intracranial space and (b) whole brain in our 116 healthy volunteers. Volume intercepts at birth are based on quantitative MR imaging findings of Huppi et al (55). (c) Graph shows that our in vivo age-related intracranial space growth rates are similar to those of previous postmortem studies (table 115 in the study by Blinkov and Glezer [19]; Lichtenberg [20]). (d) Graph shows similar growth rates in our in vivo whole brain volume (red symbols), estimated in vivo whole brain weight (black symbols; estimated whole brain weight = brain weight + CSF weight), and brain weight (open symbols) from 11 previously published postmortem studies (tables 111, 113, 115, 117, 119 in reference 19) (41-46). In the present study and in each published study, volume or weight at each age is plotted as a function of a percentage of the maximum volume or weight for that study. Postmortem weight includes CSF, but our in vivo MR imaging whole brain volume does not. Therefore, during development, CSF weight makes a small contribution to the reported brain weight, but, with aging, its weight makes a successively greater contribution. (On x axes, age scales change after 20-year point.)

View larger version (26K): [in a new window]   Figure 3a. Graphs depict growth and aging changes in (a) GM and (b) WM volumes in 116 living healthy volunteers. Volume intercepts at birth are based on the findings of Huppi et al (55). (On x axes, age scales change after 20-year point.) GM volume reached a maximum by 6-9 years of age and thereafter declined linearly. WM volume rapidly increased until 12-15 years of age, and thereafter increased at a slower rate to a plateau at about the 4th decade of life. For both (c) the present in vivo and (d) the published MR imaging and postmortem data, the GW-WM ratio drops sharply from infancy to about the end of adolescence after which it declines at a slower rate. Symbols in d represent data from the postmortem studies of Anton (49) (), Jaeger (50) (), and Miller et al (25) (*) and the MR imaging studies of Filipek et al (51) ( = data in males,  = data in females), Pfefferbaum et al (27) (), and Narayana and Borthakur (52) ().

View larger version (26K): [in a new window]   Figure 3b. Graphs depict growth and aging changes in (a) GM and (b) WM volumes in 116 living healthy volunteers. Volume intercepts at birth are based on the findings of Huppi et al (55). (On x axes, age scales change after 20-year point.) GM volume reached a maximum by 6-9 years of age and thereafter declined linearly. WM volume rapidly increased until 12-15 years of age, and thereafter increased at a slower rate to a plateau at about the 4th decade of life. For both (c) the present in vivo and (d) the published MR imaging and postmortem data, the GW-WM ratio drops sharply from infancy to about the end of adolescence after which it declines at a slower rate. Symbols in d represent data from the postmortem studies of Anton (49) (), Jaeger (50) (), and Miller et al (25) (*) and the MR imaging studies of Filipek et al (51) ( = data in males,  = data in females), Pfefferbaum et al (27) (), and Narayana and Borthakur (52) ().

View larger version (25K): [in a new window]   Figure 3c. Graphs depict growth and aging changes in (a) GM and (b) WM volumes in 116 living healthy volunteers. Volume intercepts at birth are based on the findings of Huppi et al (55). (On x axes, age scales change after 20-year point.) GM volume reached a maximum by 6-9 years of age and thereafter declined linearly. WM volume rapidly increased until 12-15 years of age, and thereafter increased at a slower rate to a plateau at about the 4th decade of life. For both (c) the present in vivo and (d) the published MR imaging and postmortem data, the GW-WM ratio drops sharply from infancy to about the end of adolescence after which it declines at a slower rate. Symbols in d represent data from the postmortem studies of Anton (49) (), Jaeger (50) (), and Miller et al (25) (*) and the MR imaging studies of Filipek et al (51) ( = data in males,  = data in females), Pfefferbaum et al (27) (), and Narayana and Borthakur (52) ().

View larger version (19K): [in a new window]   Figure 3d. Graphs depict growth and aging changes in (a) GM and (b) WM volumes in 116 living healthy volunteers. Volume intercepts at birth are based on the findings of Huppi et al (55). (On x axes, age scales change after 20-year point.) GM volume reached a maximum by 6-9 years of age and thereafter declined linearly. WM volume rapidly increased until 12-15 years of age, and thereafter increased at a slower rate to a plateau at about the 4th decade of life. For both (c) the present in vivo and (d) the published MR imaging and postmortem data, the GW-WM ratio drops sharply from infancy to about the end of adolescence after which it declines at a slower rate. Symbols in d represent data from the postmortem studies of Anton (49) (), Jaeger (50) (), and Miller et al (25) (*) and the MR imaging studies of Filipek et al (51) ( = data in males,  = data in females), Pfefferbaum et al (27) (), and Narayana and Borthakur (52) ().

View larger version (23K): [in a new window]   Figure 4a. (a) Graph shows that total intracranial CSF volume increased resolutely across the life span included in the present study. (b) Nonetheless, graph shows that the percentage of CSF occupying intracranial remained at about 7%-9% during the period of rapid intracranial space and whole brain growth (Fig 1a, 1b) from early childhood to early adolescence. Thereafter, the percentage increased as whole brain volume declined, and intracranial space volume changed little. (On x axis, age scales change after 20-year point.)

View larger version (24K): [in a new window]   Figure 4b. (a) Graph shows that total intracranial CSF volume increased resolutely across the life span included in the present study. (b) Nonetheless, graph shows that the percentage of CSF occupying intracranial remained at about 7%-9% during the period of rapid intracranial space and whole brain growth (Fig 1a, 1b) from early childhood to early adolescence. Thereafter, the percentage increased as whole brain volume declined, and intracranial space volume changed little. (On x axis, age scales change after 20-year point.)