Magnetic resonance imaging (MRI) provides us with the opportunity to study brain development within subjects over time. In longitudinal studies, participants can serve as their own control and therefore subtle changes can be identified on an individual level. Longitudinal studies have revealed that brain changes occur in childhood and in adulthood including in old age. However, to date, individual longitudinal MRI studies describe only the development in a limited age range as scanning subjects across their whole life span requires a very long interval. While having shown that brain changes occur during the whole life span, each of these studies provides a keyhole representation of these changes at a limited age span. Specifically, these studies do not allow for a direct comparison between childhood and adult changes and between young adult and old adult changes. It is during these transitions that important changes may take place. Therefore, we reviewed and integrated the findings of longitudinal MRI studies in healthy subjects throughout the life span on whole brain volume change.

Comparison of brain weight between people at different ages has suggested that considerable volume change takes place during development in humans. It has been shown in an autopsy study that included more than 4,000 individuals across the full age range that the most pronounced increase in brain weight occurs during the first 3 years of life [Dekaban, 1978 ]. Between age 3 and 18, the brain increases in weight to about 5 times that of a newborn. At ∼45–50 years of age, a progressive decline in brain weight begins and reaches the lowest values after the age of 86. By then, it is about 11% smaller relative to the maximum brain weight attained around 19 years of age. Another postmortem study suggests that it is after the age of 80 that the brain mass is rapidly decreasing [Ho et al., 1980 ]. Cross‐sectional studies suggest a linear decline in cerebral volume throughout adulthood [Raz et al., 2004 ] with an estimated volume loss of an average of 14% at age 90 [Jernigan et al., 2001 ].

For GM and WM volume change, percent change per year was calculated according to the same procedure as described previously. For those studies in the adult age range (>19 years), we smoothed the GM and WM data using the locally weighted running‐line smoother (3 dfs). The studies in childhood and adolescence were excluded from the smoothing procedure as there were too few studies that provided data on GM/WM changes.

Smoothed Q was numerically integrated to obtain volume as a function of age: Volume(age) = Volume(age0) * exp(Integrate[ Q (age')/100, {age', age0, age}]). The division by 100 is because Q is given in %; the “exp” is because integration of Q leads to the log of Volume, since Q is a relative measure. Age0 and Volume(age0) are taken from the study of 9‐year‐olds by Peper et al. [ 2009 ]; the endpoint of the integration of the younger age group was the starting point of the integration of the older age group (19 year).

For whole brain volume change, regression analysis in the form of a locally weighted running‐line smoother [Cleveland and Devlin, 1988 ; Hastie and Tibshirani, 1990 ] was used to obtain the dependence of Q on age. Software for these analyses was developed in house [van Haren et al., 2008 ]. First, the data were split into two age ranges based on the mean age of the sample, i.e., from 7 to 19 years and from 19 to 84 years. This was done because there are only a limited number of studies available covering the age range between 18 and 21 years, preventing us from making a reliable connection between development during childhood and adolescence and development during the adult age range. The degree of freedom of the fits was set to a conservative value of 3, the lowest number allowing some curvature in the fits, since there is no (statistical) support for higher values.

Whole brain, GM, and WM volume change in percent change per year () was extracted from the studies. The formula for this calculation waswith Δ2−1 representing the volume change between volume (1) at baseline (1) and volume (2) at follow‐up (2), and Δ2−1 representing the time between1 and2. For each study, mean age of the sample was defined as the mean age halfway the interval. It was measured based on either mean age at1 or2 in years (dependent on what was available) and the interval of the longitudinal measurement in years as follows: [1 + (follow‐up interval)/2] or [2 − (follow‐up interval)/2].

A systematic search was conducted to identify MRI studies that quantitatively examined longitudinal whole brain volume changes in healthy individuals with at least two MRI scans at different time points. These studies were obtained through the computerized database PUBMED for English‐language articles published until January 1, 2010. The keywords combinations used in the computerized search were “MRI and longitudinal and “whole brain,” “Brain volume change(s) and longitudinal and MRI and healthy subjects.” Articles were examined to investigate whether papers reached the inclusion criteria (see below). Additional studies were obtained by hand search of cross‐references in already identified papers.

a and b . GM and WM volume change with age. Total number of studies that presented data on GM (after excluding overlapping studies) was 10, while 9 studies presented data on WM. The relative GM and WM volume change in %/year ( Q ) was set out against the mean age in between the two time points. The zero‐line indicates no volume change. Above zero indicates an increase in the volume, whereas below zero represents a decrease in volume. The area of the circles scales as the number of subjects in the study. Fits for both GM and WM with age (with 3 degrees of freedom) were calculated to the data above age 19 (thick line). Two studies reported their GM and WM data as trajectories [Lenroot et al., 2007 ; van Haren et al., 2008 ], and these are made visible (thin lines). Liu et al. [ 2003 ] reported results for different age groups. These individual age groups are depicted separately; therefore, 12 data circles are shown for GM, while 11 data circles are shown for WM.

Figure 4 a,b shows the relative rate of GM and WM volume change per year for each individual study as circles. The area of the circle scales as the number of included subjects. A decrease is found in percent GM volume per year in all studies except for one [Lieberman et al., 2005 ]. Those studies that included individuals below the age of ∼45 years show an increase in percent WM volume per year while studies with subjects older than 45 years of age show decreases. Two studies provided age‐related trajectories of GM and WM changes with age [Lenroot et al., 2007 ; van Haren et al., 2008 ]. These are added to Figure 4 a,b.

The results indicate that a wave of growth in whole brain volume occurs during childhood and adolescence, i.e., around 9 years of age, a 1% annual brain growth is found which levels off until at age 13 a gradual volume decrease sets in. During young adulthood, between ∼18 and 35 years of age, possibly another wave of growth occurs or at least a period of no brain tissue loss. After age 35 years, a steady volume loss is found of 0.2% per year, which accelerates gradually to an annual brain volume loss of 0.5% at age 60.

Whole brain volume across the life span between 4 and 88 years of age. Whole brain volume as a function of age is obtained by numerical integration of the whole brain volume change fits with respect to age from Figure 2 . As starting volume, the mean whole brain volume from a study of N = 210 nine‐year‐old twins was used [Peper et al., 2009 ]. The curves are dashed around age 18–21, indicating the uncertainty in this area, since only few data were available for fitting this age range. Two separate fits were calculated for the younger (<19 years) and older (>19 years) group.

Fits that show the association between relative rate of whole brain volume change and age. Whole brain volume change data from the individual studies are shown as circles. The area of the circles scales with the number of subjects in the study (a larger area of the circle corresponds to more participants). Fits (with 3 degrees of freedom) were calculated to the data below and above age 19 separately (thick lines). Two studies reported their data as trajectories [Lenroot et al., 2007 ; van Haren et al., 2008 ], and these are made visible (thin lines).

Figure 2 shows the relative rate of brain volume change per year for each individual study as circles. The area of the circle scales with the number of included subjects. One study is excluded from this analysis since it deviates from all the other studies [Tang et al., 2001 ]. Therefore, fits were based on 32 studies, including 1,393 participants.

Several studies reported results for different age groups [Liu et al., 2003 ; Mueller et al., 1998 ; Scahill et al., 2003 ], and these individual age groups are depicted separately. As a result 41 data points are shown. Two studies reported their data as nonlinear trajectories [Lenroot et al., 2007 ; van Haren et al., 2008 ], and their respective trajectories are also shown here.

Longitudinal magnetic resonance brain imaging studies measuring whole brain volume change with age in humans. The total number of studies, after excluding overlapping samples, was 33. Each data point represents a study or a particular age group from an individual study. The relative whole brain volume change in %/year ( Q ) was set out against the mean age in between the two time points. The horizontal bars represent the standard deviation for age at baseline for all subjects included in the study. The zero‐line indicates no whole brain volume change. Above zero indicates an increase in the whole brain volume while below zero represents a decrease in whole brain volume. Several studies reported results for different age groups [Liu et al., 2003 ; Mueller et al., 1998 ; Scahill et al., 2003 ]. These individual age groups are depicted separately in Figure 1 ; therefore, 41 data points are shown. Two studies reported their data as nonlinear trajectories [Lenroot et al., 2007 ; van Haren et al., 2008 ], and their respective trajectories are also shown here.

Two studies were designed to investigate the effect of Apolipoprotein Epsilon e4+ (ApoE e4+) on whole brain volume change [Chen et al., 2007 ; Cohen et al., 2001 ]. Groups were either defined as ApoE e4+ carrier or noncarrier [Cohen et al., 2001 ], or subjects were divided in three groups being either homozygous (noncarriers or carriers) or heterozygous for ApoE e4+ [Chen et al., 2007 ]. The frequency of ApoE e4+ carriers in the total population of white Caucasians is 15% [Cumming and Robertson, 1984 ]. In addition, the frequency of being homozygous or heterozygous for ApoE e4+ is ∼2% and 26.5%, respectively [Hill et al., 2007 ]. Weighted means of rate of whole brain volume change per year were calculated using these known frequency distributions. For ApoE e4+ carriers [Cohen et al., 2001 ], the formula is as follows: (0.15 * Q in ApoE 4+ carriers) + (0.85 * Q in ApoE e4+ noncarriers). The formula for the study by Chen et al. [ 2007 ] is as follows: (homozygous: 0.02 * Q ) + (heterozygous: 0.27 * Q ) + (noncarriers: 0.71 * Q ).

One study included only female subjects [Cohen et al., 2001 ], and two studies included only male subjects [Pfefferbaum et al., 1995 ; Withworth et al., 2005 ]. Eighteen studies measured cerebrum volume, and the remainder of the studies ( N = 38) measured whole brain volume. As we were interested in relative brain volume change, this was not a problem in this study. Findings from 20 studies were corrected for intracranial volume (ICV) and 17 studies for baseline brain volume. Seventeen studies reported brain volume change after correcting for age and sex. An additional four studies only corrected for age, while another six studies corrected only for sex. Here, the rate of change for whole brain, GM, and WM volumes is based on the uncorrected data (if present).

The majority of the MRI studies acquired T1‐weighted images (SPGR, FFE, or MPRAGE depending on manufacturer being GE, Philips, or Siemens, respectively) on a 1.5 Tesla scanner. Brain scans were obtained in either axial, sagittal, or coronal plane with slice thickness ranging from 0.9 to 5 mm. Images were segmented using manual, semiautomated, or automated procedures or with a combination of these. For details see Table I .

Fifty‐six studies were identified as suitable for our review. Table I lists the included articles. DeLisi et al. [ 1992 ] published the first longitudinal study that included healthy controls. The number of participants per study ranged from 7 [Henley et al., 2006 ] to 228 [Lenroot et al., 2007 ]. Minimum interval between the MRI scans was 29 days [Pfefferbaum et al., 1995 ] while the maximum interval was 10 years [DeLisi et al., 2004 ]. Out of these 56, nine studies provided data on GM and WM matter volume change over time, and one study provided data on only GM volume change. Table II lists the articles with GM and WM volume change. Whenever it states “no results” in the tables for the column(s) “Relative rate of % brain/GM/WM volume change/year,” it means that there was no information available to extract percentage change per year. These studies were left out from the smoothing analysis.

DISCUSSION

In this review, findings from longitudinal MRI studies on whole brain volume change in healthy individuals over the full life span are integrated. Fifty‐six studies were selected, including a total of 2,211 healthy individuals. We find that brain volume changes throughout life, not only in childhood and adolescence but also in adulthood. More specifically, the results indicate that brain volume increases during childhood and young adolescence until the age of ∼13 years. After age 13 years, a decrease in whole brain volume sets in. The main finding from the current study is that we provide evidence for a possible second wave of brain volume growth or at least a stable period in early adulthood preceding a brain volume decrease from the age of 35 years with accelerating tissue loss occurring with increasing age.

As is shown in Figure 1, there are three studies that show brain growth in young adulthood [age 20–30 years; Ho et al.,2003; Lieberman et al.,2005; Whitworth et al.,2005; symbols above zero], and two studies showing a decrease [DeLisi et al.,1997; Liu et al.,2003; symbols below zero]. Fitting the data from these studies suggests that brain volume slightly increases over time in this age range, but given the fact that there is no agreement between the different studies, we interpret this result with caution and suggest a possible growth or a plateau period where no change in volume takes place.

Most likely these whole brain volumes changes are a net consequence of many different factors such as focal growth and shrinkage of GM and WM. Until now, only a limited number of studies have investigated GM and WM volume change over time in longitudinal studies. Our findings suggest an increase in GM volume in childhood after which a decrease sets in, while WM increases till the age of ∼45 years and thereafter starts to decrease.

Indeed, the NIMH group showed that cerebral GM volume increases in preadolescence [Giedd et al.,1999a,b; Gogtay et al.,2004; Lenroot et al.,2007; Sporn et al.,2003], after which it starts to decrease in postadolescence, while cerebral WM continues to increase in volume [Giedd et al.,1999b]. It has been suggested that GM is replaced by WM in childhood and adolescence [Giedd et al.,1999b; Jernigan et al.,1991]. In line with our findings, others have suggested that GM shows a linear age‐related decrease in adulthood but an increase in WM until midlife which started to decrease after that in an inverted U‐shaped curve [Taki et al.,2009; van Haren et al.,2008]. If indeed there is subtle brain growth in young adulthood as is suggested by the plots in the current review, this might then be explained by WM increasing more than GM is decreasing.

Most studies did not provide sufficient data to investigate differential effects between males and females while those that did report inconsistent findings. One study showed a greater decline in cerebral volume in girls as compared with boys in cerebral volume during the 2nd decade of life [Lenroot et al.,2007]. The developmental trajectories suggest that brain volume in girls peaked at 10.5 years while that in boys peaked at 14.5 years. One study, however, was not able to replicate this in children between the ages of 8 and 12 years [Ment et al.,2009]. In older age, a significant sex difference with more pronounced volume loss in males relative to females has been reported [Driscoll et al.,2009]. However, most studies suggested that in adulthood and old age rate of change in whole brain volume is similar in males and females [Autti et al.,2008; Chan et al.,2001; DeLisi et al.,2004; Fotenos et al., 2005; Fotenos et al.,2008; Liu et al.,2003; Resnick et al.,2003; Ridha et al.,2006; Scahill et al.,2003; Tang et al.,2001]. Here, we focus on volume change in global measures such as whole brain or GM and WM. Males do indeed have larger brains, but the available literature in adults on gender differences suggests that males and females have similar rates of change in global brain volume measures.

As to the mechanisms underlying the brain changes throughout life, we can only speculate at this point. Our finding of brain changes over the entire life span suggests continuous brain plasticity throughout life. Brain plasticity can be referred to as the changing of neurons, the reorganization of their networks, and their change in function as a consequence of new experiences. Obviously and fortunately, experiencing new events does not stop at reaching adulthood. In the aging brain, it has been proposed that cell shrinkage, degeneration of key neurons, and circuits could explain the age‐related decrease in brain volume [Morrison and Hof,1997]. We recently found that genes are implicated in brain structure changes in adulthood [Brans et al.,2010]. In contrast, there is no evidence that it is synaptic density that explains the adult volume change since a postmortem study showed that the synaptic density is constant throughout adult life (ages 16–72 years) [Huttenlocher,1979]. Disentangling the processes underlying brain plasticity is important for healthy development and also provides insight into what may be arrested in brain disorders that have their first symptoms at a particular age.

There are several limitations to the study that have to be considered when interpreting its findings. A limited number of studies were available covering the age range between 18 and 21 years, preventing us from making a reliable connection between development during childhood and adolescence and development during the adult age range. For this reason, the data were split into two age ranges. However, since the (mathematical) integration was determined over the full age range, and only the integrand (i.e., the rate of change) was derived from two separate regressions, we can conclude that after the growth wave during childhood and adolescence, a second wave of growth or at least a plateau period occurs in young adulthood. Since there remains some uncertainty as to what exactly happens between 18 and 21 years, this finding requires confirmation from longitudinal study in subjects between 16 and 25 years.

A few studies did not fit the regression line well and can be considered as outliers. One study included subjects over the age of 80 years and found remarkable losses in brain volume of 2.1% per year [Tang et al.,2001]. An explanation for this finding may be that it is the only study that used the Cavalieri method to estimate the whole brain volume. One study included subjects with a mean age of ∼25 years and reported a relatively large decrease of −0.70% per year [DeLisi et al.,1997]. Consideration mentioned by the authors was possible artifacts in the scanner over time [see DeLisi et al.,1997].

The differences in acquisition methods and study design may have affected the results, specifically the regression lines. Studies with high‐resolution scans are expected to be more sensitive to brain volume changes than studies using thicker MRI slices. The high/low‐resolution studies were more or less evenly distributed over the age range. Studies with large age ranges have to be dealt with carefully, since they tend to average out the change‐rate‐per‐age values. In our analysis, we assume that the scan intervals are short enough (≤ 6.5 year) so that between the first and the last scan, the rate of change will not differ much. For rapidly changing rates and for large age ranges, this assumption is violated. In two studies, this assumption could be violated [Lenroot et al.,2007; van Haren et al.,2008]. However, these two studies applied an age‐dependent analysis, which we incorporated in full in our analysis. Another six studies that had an age range >20 years: [Driscoll et al.,2009; Fotenos et al.2005; Fox et al.,2000; Goldstein et al.,2005; Liu et al.,2003; Silbert et al.,2008]. After inspection, these studies do not seem to violate the assumption of piecewise constant change rate.

Another issue concerns the definition of “healthy individual,” which differed between studies. Some studies excluded participants with a psychiatric history or hypertension, while others did not obtain this information or performed population‐based studies. In addition, even when using the same scale (such as the MMSE) as a screening, the cutoff for inclusion differed between studies (e.g., 24 for the MMSE in two studies [Mueller et al.,1998; Silbert et al.,2008] and 28 for one other study [Rusinek et al.,2003]. Finally, with increasing age, the incidence of hypertension, diabetes, WM hyperintensities (WMH), and cognitive decline most likely increases, so that the inclusion of healthy participants at an older age may be limited to a shrinking population of people that remain (extremely) healthy throughout life. As can be seen from Figure 2, there appears to be a flattering of the decrease in brain volume after the age of 75 years. This might be explained by the inclusion bias mentioned. It is likely that extremely healthy participants are selected in the age range over 75 years of age. Only those who are physically healthy (can) participate while functionally impaired subjects cannot.

When interpreting longitudinal follow‐up studies using complex techniques such as MRI, one always has to take into consideration that noise is introduced by changes in the measurement over time. However, it is unlikely that such limitations of the study significantly influenced the results of our analysis. Because of the large number of studies that were included, one would expect noise to be largely leveled out. We were able to plot the trajectories of whole brain volume change with age that were present in two studies onto our plot based on all studies. Comparison of these fits with the overall fit shows that both trajectories, i.e., one during childhood and adolescence [Lenroot et al.,2007] as well as one in adulthood [van Haren et al.,2008], agree nicely with the brain volume change based on all the included longitudinal MRI studies.

In conclusion, we reviewed and integrated the findings of 56 studies investigating longitudinal whole brain volume change. The results indicate that whole brain volume change is an ongoing process throughout the full life span with increasing brain volume in childhood and adolescence in the age of ∼13 years after which a rapid volume decrease sets in. We found suggestive evidence of a second period of growth or at least stability in brain volume. It is only after the age of 35 years, the brain starts to decrease in adulthood. Longitudinal studies that include subjects between 15 and 25 years of age are needed to confirm our finding of a stable or growing whole brain volume during this possibly critical stage of brain development. The results may help in understanding the mechanisms of normal brain changes and may contribute to distinguishing psychiatric and neurodegenerative diseases from healthy aging processes.