Phrenoblysis: Special Brain and Mind Growth Periods. I. Human Brain and Skull Development

H. T. EPSTEIN Biology Department Brandeis University

Waltham, Massachusetts

Although gross brain properties averaged over many individuals are highly unlikely to exhibit possible special growth periods, the hypothetical spurts in brain growth can be recovered to some extent from averased data by computing multiyear weight increments. And, since brain weight correlates very well with skull circumference, similar spurts may be found in that parameter. A review of the literature shows that, indeed, characteristic spurts in brain and skull occur, roughly, at ages 6-8, 10-12, 14-17, and possibly 2-4 yr. The spurts are, as expected, especially clear in data from longitudinal studies, although spurts are detectable in the data from every study thus far found in the literature.

The determination of the biological organization and phenomena underlying the development of intelligence in humans seemingly must await a far more detailed understanding of the functional organization of the brain than we now possess. Nevertheless, some behavioral data and some general ideas about evolution of brains have led to a working hypothesis whose validity is assessed in this paper. The hypothesis is that human brains have periods of specially large increases in weight that are uncorrelated or only weakly correlated with periods of general body growth.

We will use the term “phrenoblysis” for the spurts in brain and mind. “Phreno” comes from the Greek word meaning skull or mind, whde “blysis” indicates a welling-up of matter.

Although nature builds on single small mutations, a mutation to be selected for preservation must lead to an appreciable advantage for the organism. Changes in just a relatively few cells in a large and organized structure like the human brain are not likely to confer such an advantage. Thus, advantageous brain mutations will be ones that affect basic processes such as, for example, the organizational connectivity of brain cells or the regulation of myelination. For example, if a mutation affects the cerebellum, the associated advantage should derive from alterations encompassing the entire cerebellum or from splitting of the cerebellum into several portions or from a splitting into portions one or more of which might contain cells with a novel organization feature. Such novel organizational features may well underlie the variations in sulcus formation that occur in the phylogeny of some animal brains.

In reviewing the literature relevant to such a hypothesis, we must search for data leading to indications that the worlung hypothesis might be wrong. To avoid any possible

Received for publication 5 September 1972 Revised for publication 15 May 1973 Developmental Psychobiology, 7(3): 207-216 (1974) @ 1974 by John Wiley & Sons, Inc.

207

208 EPSTEIN

bias, we will examine all studies found that relate to the question of ontogeny of brain weight and head size. (Of the 14 such studies that have been discovered, none yields data that contradict the working hypothesis.)

Gross physicochemical properties of brains (such as weight, DNA content, metabolic acitivities, oxygen and glucose uptake) would seem unlikely candidates for properties that could even quantitatively reflect intellectual activities and their embryological changes. This follows because: (1) the great variability of brain weights among humans (1 100-2200 g) hides relatively small specific variations, and (2) the very high metabolic activities in brains hide specific metabolic changes much more than in organs with more typical metabolic rates.

At least one clear route of escape remains from the constraints just mentioned: if developmental changes exist, they may occur at similar ages regardless of the actual sizes and functioning of the various individual brains. Thus, changes in brain properties may well occur in detectable spurts. An example of already known properties that spurt at an early age is myelination, which accounts for much of the increased mass of the brain that develops between birth and age 4 yr. A mutation in the regulation system for myelination could easily affect the size of a nonnegligible portion of the brain and simultaneously alter neural network responses in a significant way.

We have 2 directions from whch to approach the question of such hypothetical spurts. The 1st would be to measure brain properties at various ages looking for characteristic spurt periods. The 2nd would be to examine behavioral data on the grounds that fairly abrupt changes in behavior are likely to reflect associated changes in biophysical properties of brains, thereby indicating the ages at which to look for possible spurts. On this 2nd strategy, classification schemes such as those of Gesell and Piaget are available.

We will look first at biophysical properties to determine the possible spurt periods and then use mental behavior information to infer the significance of the brain spurts in terms of mental activities. We will eventually examine aspects of schooling which are part of the psychology of learning in that they pertain to what subjects and thought strategies may be taught at what ages. This is really to be called the psychology of schooling.

If characteristic and developmentally determined stages of special brain growth exist, these spurts can be detected. First, however, we must mention that brain weights have thus far been determined only on recently dead persons. Thus, we have no data for the brain weights of single individuals throughout their lives. Indeed, we have presently no developed methods for determining weights without doing appreciable harm to the persons involved, so that even if, for example, we could accurately determine brain sizes by x-ray methods, the resultant brain damage would make the methods unacceptable. Accordingly, the data for brain weights at various ages are averages over many individuals. If the spurt periods of individuals vary somewhat, the averages will show a smooth increase in brain size. However, we can recover the spurts from the smoothed data as now explained by use of a hypothetical numerical example.

Let us suppose that the brain spurts themselves take 6 months to complete. Let us suppose further that no person starts a brain spurt before age 10 yr and no one finishes a spurt later than age 13 yr. The difference in average brain weight over the 3-yr period between 10 and 13 gives the entire average brain spurt, for every individual will have started and completed his spurt in this time. If, however, we consider the period between ages 9 and 12 (or between 11 and 14), about one-third of the individuals will not have

BRAIN AND SKULL GROWTH SPURTS IN HUMANS 209

had their brain spurts during the given interval, so the measured difference in average brain weights will be about two-thirds of that found between ages 10 and 13. Similarly, the intervals 8-1 1 and 12-15 will show about one-third of the spurt total. Thus by taking triennial increments in brain weights, we will recover the spurt as a symmetrical peak centered on the middle point of the spurt age. I f we do not know the interval over which the spurts occur, we just compute the annual, biennial, triennial, and so forth, differences until we find the hypothesized spurts. In all the data to be discussed in this paper, biennial increments will be presented since these have been found adequate for these purposes .

This procedure for obtaining spurts is not equivalent to the usual velocity determinations because velocities give information about the change in the property being studied. Our procedure yields the fraction of individuals who have started and completed the change during the time interval in question, although the value at the peak is an accurate estimate of the average spurt magnitude. If velocities were desired, we would have had to face the fact that the average of a velocity is not equal to the velocity of the average. Another reason for choosing biennial increments is that experimental errors made in 1 yr will, in many instances, be compensated for by correct determination in the following year so that the 2-yr increment will be accurate even though 1-yr increments may be subject to error.

Data and Analysis

Search of the literature has turned up only 1 set of data giving brain weight for every year up through age 20. These data are given by Boyd (1962) and are presented in Figure 1A; values for males and females are given separately because, although both sexes have the same average newborn brain weight, they do differ substantially (about 100 g) as adults. At present, we have no data permitting us to know if the observed difference is due to different numbers of cells, different sizes of cells, or both.

The lines have been drawn through the data points even though a reasonable procedure would be to draw a line representing the smoothed data. If these data were to be the only ones available for analysis, we would have no reason for further analysis.

Braidbody weight ratios have been given by Dullemeijer (1971) using the Boyd data for the average brain weights at each age. Use of the ratios automatically factors out the effect of general body growth, though there is no reason for requiring that brain spurts be not associated with general body growth. However, as will be demonstrated, the observed spurts in the ratios occur at times differing appreciably from those concerned with general body or organ growth.

When these ratios are analyzed for biennial increments (in this case, decrements) of the brain/body weight ratios, the results are as given in Figure 1B. Here, spurts are seen to occur at ages 6 , 8, 11, and 15 yr. The low slope from 2-5 yr is indicative of the existence of a growth peak in the age span because otherwise the numbers would have dropped as they do at other slow growth periods found at ages 7, 10, and 13 yr. A similar remark is in order with respect to the data for growth between ages 16 and 18 yr. Because the spurt at 8 yr is given by a single point, it is therefore suspect.

The actual magnitudes of the spurts at those periods are between 35 and 85 g, amounting to about 3-8%; 4 such spurts sum to more than 25%. The brain weight at age 2

210 EPSTEIN

r

A

4 ' I ' I ' I ' I ' I ' I " ' I " I

0 2 4 6 8 10 12 14 16 18 AGE (YEARS) AT MIDPOINT OF BIENNIAL SPAN

Fig. 1. A-Average brain wei.&t at various ages for males (X) and females (0) (Boyd, 1962). B-Biennial increments in brain/body ratio expressed as the percentage increase over that expected from slope at that age (Dullemeijer, 1971).

is about 1050 g, so the 260 g added in spurts bring the brain weight up to about 13 10 g. Because this value approaches the adult brain weight, these spurts account for most though not all of the increase in brain weight after age 2 yr.

The accuracy of the data depends on the numbers of individuals measured at each age; this number varies from about 20 to 100. Since the standard deviations are about 100 g each, the standard errors of the means are about l/5th to l/lOth of the standard deviations, or 10-20 g. Since the biennial increments are as great as 85 g, they are unlikely to be due simply to random fluctuations in the data.

The 2nd way of handling the problem of general body growth is to compare brain growth with that of the body and its various components. Reed and Stuart (1959) have measured the height and weight of 134 children during their growing years to adulthood. Their data show a maximum growth phase at about age 13 yr. This age is not similar to any of those shown in Figure I H , as expected, because brain/body ratios were used.

Coppoletta and Wolbach (1933) have given data for various body parameters up through age 12 yr. Their data are given in biennial increment form in Figure 2 for brain, body length. lung, liver, heart, and kidney. The brain data show a spurt up to age 11, and

BRAIN AND SKULL GROWTH SPURTS IN HUMANS 2 1 1

2 4 0

160

5 4 0 x LUNGS-GRAMS

LIVER-GRAMS/IO 0

X H x \ x 2 0

\ KIDNEY-GRAMS X.x/x\x \.-a/

0 ' ' ' I ' I ' I ' I * I 0 2 4 6 8 1 0 1 2

AGE(YEARS1 AT MIDPOINT OF BIENNIAL SPAN

Fig. 2. Biennial increments in various body parts (Coppoletta & Wolbach, 1933).

a smaller spurt at age 7. The 3-4 yr spurt is also shown by kidney, liver, heart, and body length. Only the liver and possibly the heart show a trace of the spurt at age 7. Thus, the periods of special growth of various organs and the whole body do not generally coincide with the characteristic growth periods of the brain. Data from Feer quoted by Coppoletta and Wolbach (1933) agree very well with their own brain and body height data. Thus, except for the possible small spurt for age 11 female hearts and the liver spurt at age 7, the general growth spurts of the body do not coincide with the inferred brain growth spurts at ages 6-8, 10-12, and 14-16 shown in Figures 2 and 3. The magnitudes of the brain spurts in Figure 2 are about 30 and 60 g, yielding percentages of 2.5 and 5, respectively, which are similar to the values obtained from the data of Figure 1.

Blinkov and Glezer (1968) give brain weight data for a range of ages similar to that of Coppoletta and Wolbach (1933). These studies also show positive spurts in average brain weight at ages 7 and 11 yr. Taken together, the 6 brain weight studies indicate the existence of spurts at ages 3, 7, 11, and 15-16 yr, computed as biennial increments.

To proceed with the question of the realness of the spurts, we must turn our attention to parameters other than brain growth itself.

212 EPSTEIN

1001

- v) I a a - I-

5 IOC - W 3 f a m 4

10 20 40 60 ao 100 HEAD CIRCUMFERENCE (CMS)

Fig. 3. Relationship between logarithm of brain weight and logarithm of head circumference (Winick, 1968).

To go further requires evidence that another paranienter can be related quantitatively to brain weight. Such evidence exists for skull circumference measured just above the ears. An increase in brain size should cause an increase in skull dimensions; the quantitation of this relation or correlation has recently been studied for humans at ages up through 15 months. Winick and Rosso (1969) measured both brain weights and skull circumferences of accident victims and their data are given in Figure 3 , which is a plot of log brain weight against log skull circumference. The dashed lines show the theoretical lines for lst, 2nd, 3rd, and 4th power relationships. As can be seen from the data, the 3rd power relationship fits very well indeed. No further evidence is available to extend the relationship, but we expect a similar relationship over any few years because thickening of skull bones is not a particularly prominent feature of short spans of time (Roche, 1953). Accordingly, we can use the skull circumference as an estimate of what is happening to the brain weight.

We have located 8 separate studies of head size. To avoid having to present all the individual studies. they have all been normalized by taking the maximum increment as 1 .O and the smallest as 0.0. The brain data presented have been siniilarly normalized. Taken

BRAIN AND SKULL GROWTH SPURTS IN HUMANS 2 I3

.8

.6

.4

.2

l.o/

-

-

-

-

0'

T -

PEAKS (4) (3) (1) (3) (1) (1) (2) STUDIES 9 10 10 11 11 11 10 10 9 9 8

L I i t , I I I I I , I I I I I I L

I

Fig. 4. Normalized biennial increments in brain weight or skull circumference. The standard errors for each age are given. The numbers in parentheses are the numbers of studies at each age which have the main growth peak at that age. The figures outside parentheses give the number of studies averaged for each age.

together, the results have been averaged and are given in Figure 4 along with the standard errors based on the variations among the averages of the various studies.

Despite the fact that the analysis averages over longitudinal and cross-sectional studies, over brain growth and skull growth, and over the male-female differences, the brain-skull spurts at ages 7, 11, and 15 yr are clearly exhibited. The rapid physical growth around age 3 yr precludes definitive inferences concerning spurts at that age.

TABLE 1. Ages o f Peaks or Troughs in Biennial Increments in Brains and Skulls,

Age at Midpoints of Biennial Span

- Reference - __ 3 5 6-7 8-10 11-12 13-14 15-17 Boas, 1912

Hebrews + -

Sicilians + Central Europeans +

Coppoletta and Wolbach, 1933 -.

Shuttleworth, 1939 -

Simmons, 1944 - Reynolds and Schoen, 1947 + - Westrop and Barber, 1956 + Dokladal, 1959 +? - + + Bayley and Eichorn, 1962 + - Dullemeijer, 1971 + - + -

+ +

Vickers and Stuart, 1943 + + i

+

+ + +

- + +? +

+ .-

-? + + -

+ + +

.-

.-

214 EPSTEIN

Discussion

The existence of phrenoblysis in developing humans can be based either on the correlation of spurts among the various studies surveyed or on the validity of the measured spurts established by analysis of the data of the individual studies. Here, the validation will be based on both approaches.

The data in Figure 4 give clear evidence of growth spurts and lags in brain and skull growth. The correlation among such studies must be substantial to avoid blurring of the peaks and troughs because of the wide range of studies that went into the figure. In the figure there is a set of numbers giving the number of studies in which the normalizing value of 1 .O occurred at each age. It is seen that 7 studies give maxima around ages 6 and 7, 5 around age 11 , and 3 around age 14-1 5 yr, showing that the individual studies have the same growth spurts as found for the entire group.

This individualization of the data can be carried further by handling the data so as to exhibit the individual peaks and troughs of the various studies. Table 1 gives such information for all the studies surveyed for this paper. The peaks are symbolized by (+), while troughs are symbolized by (-); a question mark indicates marginal evidence at the indicated age range.

It can be seen that there is evidence in every study that reaches age 18 for a growth peak in the age range 15-17, with most studies showing a trough around age 13-14. Virtually all show a peak at ages 11-12 and 6-7. Most show evidence for a 2nd trough around age 8-10, and there is some tentative support for a spurt around age 3 yr.

Thus, the individual studies show the same pattern of phrenoblysis as do the normalized data of Figure 4.

Phrenoblysic growth can account for most of the increase in brain and skull dimensions after age 2 or 3 yr. The phrenoblysic magnitudes all agree in being between 3 and 9%, an agreement which gives additional support to the hypothesis that the brain and skull increments are not artifacts of the data. The standard deviations of the individual measurements tend to be about 596, but the standard errors of the means are appreciably less than that value and are also appreciably less than the spurt magnitude.

The sources of the growth are not presently known, though Yakovlev and Lecours (1967) have indicated that much or most of the brain weight increase between ages 1 and 4 yr is due to myelination. The ratio of FWA to DNA in human brains through age 1 yr has been shown (Winick, 1968) to be about 1, but has a value of 60 or more (Hyden, 1962) starting with age 3 yr. Thus, apparently more than myelin is involved. The very large increase in RNA between ages 1 and 3 yr amounts to about 300 pg/cell; for 10" cells this sums to about 30 g, which is similar in magnitude to the measured brain weight increase. Thus, myelin and FWA would appear to be the major components. However, if the RNA is functioning in protein synthesis, one would expect protein synthesis of a similar magnitude so that all 3 of these macromolecular components are likely to make up the measured increase in brain weight around age 3.

This phrenoblysic age spectrum for humans might be correlated with spurts in mental abilities. On the basis of anecdotal evidence obtained by talking with teachers, we have reason to believe that the periods are correlated with spurts in learning ability. The 14-1 5 yr spurt is correlated with the Piagetian stage of formal operations, which is generally supposed to initiate after age 12. The 11-yr spurt is correlated with the rapid growth of conceptualization about concrete objects in the environment, which is utilized in schools

BRAIN AND SKULL GROWTH SPURTS IN HUMANS 2 15

to build in, for example, ideas about fractions and geometrical objects. The 7-yr spurt coincides with the start of formal learning normally associated with the acquisition of reading and writing skills by the average child.

In addition, we may study development of the nonhuman primate brain to ascertain whether similar phreiioblysis occurs. Harlow, Harlow, and Suomi (1971), among others, pointed out the occurrence of stages of learning capacities in monkeys; thus, we may expect to find brab growth spurts in these species as well.

Notes

I am deeply grateful to Prof. Jerome Y. Lettvin for the time so generously given for critical discussions of both the generalities and the details of this work. Dr. David Wiesen supplied a general insight of help to me. Prof. Johns Hopkins precipitated much of the work by inviting me to talk about the subject at a time when its scientific aspects were only incompletely worked out.

Present address of author: Microbiology Department, Tel-Aviv University, Tel-Aviv, Israel. Request reprints from Secretary, Biology Department, Brandeis University, Waltham, Massachusetts 02154, U.S.A.

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