# data[].attributes.body
To Queenie

https://www.sciencedirect.com/science/article/pii/S187892932100116X

To Eva

https://pmc.ncbi.nlm.nih.gov/articles/PMC6666647/

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Abstract

Brain development during late childhood and adolescence is characterized by decreases in gray matter (GM) and increases in white matter (WM) and ventricular volume. The dynamic nature of development across different structures is, however, not well understood, and the present magnetic resonance imaging study took advantage of a whole-brain segmentation approach to describe the developmental trajectories of 16 neuroanatomical volumes in the same sample of children, adolescents, and young adults ( n = 171; range, 8-30 years). The cerebral cortex, cerebral WM, caudate, putamen, pallidum, accumbens area, hippocampus, amygdala, thalamus, brainstem, cerebellar GM, cerebellar WM, lateral ventricles, inferior lateral ventricles, third ventricle, and fourth ventricle were studied. The cerebral cortex was further analyzed in terms of lobar thickness and surface area. The results revealed substantial heterogeneity in developmental trajectories. GM decreased nonlinearly in the cerebral cortex and linearly in the caudate, putamen, pallidum, accumbens, and cerebellar GM, whereas the amygdala and hippocampus showed slight, nonlinear increases in GM volume. WM increased nonlinearly in both the cerebrum and cerebellum, with an earlier maturation in cerebellar WM. In addition to similarities in developmental trajectories within subcortical regions, our results also point to differences between structures within the same regions: among the basal ganglia, the caudate showed a weaker relationship with age than the putamen and pallidum, and in the cerebellum, differences were found between GM and WM development. These results emphasize the importance of studying a wide range of structural variables in the same sample, for a broader understanding of brain developmental principles.

Introduction

Subcortical structures are important in developmental disorders ( Krain and Castellanos, 2006), and we need a better understanding of the dynamics of differential subcortical brain development. However, few magnetic resonance imaging (MRI) studies cover normal brain development in multiple subcortical regions in childhood and adolescence. The aim of the present study was to describe and compare the developmental trajectories of several subcortical structures in the same sample of participants, thereby shedding light on the dynamic interplay between different brain structures in development.

It is generally acknowledged that gray matter (GM) volume increases in early childhood and decreases during adolescence ( Jernigan et al., 1991; Reiss et al., 1996; Giedd et al., 1999; Sowell et al., 1999, 2002; Lenroot et al., 2007; Wilke et al., 2007; Shaw et al., 2008). This may, in part, reflect early synaptic arborization and later pruning ( Huttenlocher, 1990). Results indicate that subcortical GM structures show differential developmental trajectories ( Giedd et al., 1996b; Sowell et al., 2002; Toga et al., 2006). Because most studies have quantified only one or a few structures, the relative development of the subcortical structures is still unclear, however. For instance, different emphasis has been put on either the caudate or lenticular nuclei in development in various reports, sometimes because of methodological constraints ( Jernigan et al., 1991; Giedd et al., 1996a; Sowell et al., 1999, 2002; Wilke et al., 2007). There has been some focus on structural development of the cerebellum ( Sowell et al., 2002; Liu et al., 2003; Mackie et al., 2007) and accumbens ( Sowell et al., 2002), but cerebellar development is still not characterized in terms of separate WM and GM development. Furthermore, there is a need for a broader understanding of the development of hippocampus and amygdala, as these structures often appear to increase during development, although discrepancies exist ( Giedd et al., 1996b; Sowell et al., 2002; Gogtay et al., 2006; Toga et al., 2006; Guo et al., 2007). It is not clear how this is to be understood within a (proposed) framework of arborization/pruning as general principles of GM development. White matter (WM) volume has been shown to increase, often linearly, probably driven by increased myelination during adolescence and early adulthood ( Reiss et al., 1996; Giedd et al., 1999; Sowell et al., 2002; Lenroot et al., 2007). However, developmental trajectories for WM in the cerebrum versus cerebellum have not been compared.

In the current study, maturational changes were described and compared across volume estimates for the caudate, putamen, pallidum, accumbens area, amygdala, hippocampus, thalamus, cerebellar cortex and WM, brainstem, cerebral cortex and WM, and ventricles (lateral ventricles, inferior lateral ventricles, third ventricle, fourth ventricle), along with volume, thickness, and surface area estimates of the cortical lobes. Especially, it was investigated how subcortical structures develop compared with the cerebral cortex, how the basal ganglia/striatal structures compare in development, how the amygdala and hippocampus develop, and how cerebellar development can be described in terms of separate GM and WM volume.

Materials and Methods

Figure 1.

Regression plots showing the relationship between age and bilateral volumes of thalamus (a), cerebral cortex (b), cerebral WM (c), cerebellum GM (d), brainstem (e), cerebellum WM (f), caudate (g), putamen (h), the accumbens area (i), pallidum (j), amygdala (k), and hippocampus (l), with age on the horizontal axis and TBV-corrected volume in z-scores on the vertical axis. Also displayed are samples of the segmentation (cerebral WM not shown), based on the group mean.

Figure 2.

Regression plots showing the relationship between age and bilateral volumes of the lateral ventricles (a), the inferior lateral ventricles (b), the third ventricles (c), and the fourth ventricles (d), with age on the horizontal axis and TBV-corrected volume in z-scores on the vertical axis, shown together with samples of the segmentation, based on the group mean.

Results

Descriptive analyses

ANOVA with 13 bilateral brain volumes and two hemispheres as within-subjects factors and age as the between-subject factor showed no significant hemisphere × age interaction effects ( F(124,46) = 1.104; p = 0.36). This analysis was repeated for four bilateral regional (cortical lobe) variables, yielding no significant hemisphere × age interaction effects for volume ( F(124,46) = 0.885; p = 0.706), thickness ( F(124,46) = 1.269; p = 0.179), or surface area ( F(124,46) = 1.092; p = 0.375). Thus, the sum of right- and left-hemisphere volumes was used in all analyses. As expected, males had larger TBVs than females as evidenced by an independent-samples t test (males: 1,290,900 mm ³; SD, 87,897; females: 1,152,300 mm ³; SD, 80,731; t₍₁₆₉₎ = −10.75; p = 0.0001). Also, as can be seen from Table 2, the variance was high within the age groups. Analysis of covariance with 16 TBV-corrected brain volumes as the within-subjects factor, sex as the between-subjects factor, and age as a covariate showed no sex × age interaction effects ( F(22,23) = 0.68; p = 0.82) or sex × age × structure effects ( F(182.737,191.043) = 1.141; p = 0.183). This analysis was also performed with the regional cortical variables, revealing no significant sex × age effects in thickness ( F(22,23) = 0.876; p = 0.743), surface area ( F(22,23) = 0.867; p = 0.630), or TBV-corrected volumes ( F(22,23) = 0.852; p = 0.645) or sex × age × structure effects for thickness ( F(53.864,56.312) = 0.963; p = 0.554), surface area ( F(44.832,46.869) = 0.823; p = 743), or TBV-corrected volumes ( F(42.731,44.673) = 0.881; p = 0.660). There may still be sex effects of interest in select structures. However, since no interaction effect of sex and age was shown in this overall analysis, and sex is not the primary focus of the present study, additional analyses were performed on males and females together.

Table 2.

Age groups

8-11 (n = 38)

12-15 (n = 37)

16-19 (n = 36)

20-23 (n = 33)

24-31 (n = 27)

Total (n = 171)

Relationships between age and brain volumes

The volume for each brain structure in each of five age groups is displayed in Table 2. The volume, thickness, and surface area of each cortical lobe and total cerebral cortex are displayed in supplemental Table 1 (available at www.jneurosci.org as supplemental material). The results of the regression analyses with subcortical brain volumes are shown in Tables 3 and 4, whereas Table 5 shows the results of the regression analyses with cortical variables (volume, thickness, and surface area). In the raw volume analyses, all GM volumes, except hippocampus, amygdala, and thalamus, showed a negative relationship with age ( p < 0.05). Amygdala and hippocampus showed a slight positive relationship with age, but only the amygdala increase was significant. The cerebral cortex was the only GM structure that showed a curvilinear relationship with age. This pattern was seen in the parietal and occipital lobe volumes in the regional cortical analysis, whereas the frontal and temporal lobes were best described by linear age relationships. In the cortical thickness analysis, the frontal, parietal, and occipital lobes showed quadratic age relationships, and the temporal lobe showed a linear age relationship. All lobar surface areas showed statistically significant age relationships, and the parietal lobe surface area also showed a quadratic relationship with age ( p < 0.05). All WM volumes (cerebral WM, cerebellar WM, brainstem) showed a quadratic relationship with age ( p < 0.05). The lateral ventricles increased linearly with age, whereas the inferior lateral ventricles and the third and fourth ventricles showed no significant relationship with age. With Bonferroni corrections, the amygdala and caudate age relationships no longer reached significance in the volumetric analysis, whereas the additional contribution of age ² to the explained variance in the parietal surface area disappeared. When introducing age ³ as a third regressor, an additional unique variance ( p < 0.05) was explained in only one structure, the pallidum, for which R ² increased from 0.10 to 0.13 ( p = 0.02) (not displayed in the tables). The regression plots for the raw volumes are displayed in supplemental Figure 1 (available at www.jneurosci.org as supplemental material). The regression plots for the regional cortical variables (thickness and surface area) are displayed in Figure 3.

Table 4.

Regression with age

Regression with age and age²

β

F

p

R ²

F

p

R ²

p age2a

Table 5.

Regression with age

Regression with age and age²

β

F

p

R ²

F

p

R ²

p age2a

Figure 3.

Regression plots showing the relationship between age and bilateral cortical volume (TBV corrected), thickness, and surface area for each cortical lobe and for the total cerebral cortex, with age on the horizontal axes. The measurements are standardized residuals (volumes), in millimeters (thickness), and in square millimeters (surface area) on the vertical axes. The parcellation of the cortical lobes are displayed in the left column.

When the analyses were repeated with the TBV-corrected volumes as the dependent variables ( Tables 4, 5), the finding that most GM volumes decreased with age while the WM volumes increased was replicated. However, hippocampus showed a significant positive relationship with age, and both the hippocampus and the amygdala showed a nonlinear increase in volume. The regional cortical analysis revealed statistically significant contributions of age ² in all four cortical lobes. As in the analyses with the raw volumes, an additional unique variance ( p < 0.05) was explained in only the pallidum when introducing age ³ as a third regressor, with R ² increasing from 0.11 to 0.13 ( p = 0.048) (not displayed in the tables). The amount of variance explained by age tended to increase for the cerebral cortex and WM, cerebellum WM, brainstem, amygdala, putamen, lateral ventricles, and hippocampus when TBV-corrected volumes were analyzed compared with the raw volumes. When the results were Bonferroni corrected, the TBV-corrected caudate age relationship no longer reached statistical significance, and the contribution of age ² to hippocampus, amygdala, and temporal lobe also disappeared, as did the cubic component in the regression with pallidum. Regression analyses were also performed with TBV, age, and age ² in the model, to enable evaluation of these separate components, and the results are shown supplemental Table 2 (available at www.jneurosci.org as supplemental material). Generally, TBV showed a profound effect on individual brain variables. The TBV-corrected cerebral cortex (frontal and parietal lobes more than temporal and occipital lobes), cerebral WM, brainstem, cerebellar WM, and amygdala showed the greatest impact of age in terms of explained variance, whereas the hippocampus, the cerebellum cortex, and the caudate showed the least impact of age in this analysis. The regression plots for TBV-corrected GM and WM volumes and ventricles are displayed in Figures 1 and 2. The regression plots for the TBV-corrected regional cortical volumes are displayed in Figure 3.

The results of the regression analyses with proportional volumes of TBV are displayed in supplemental Table 3 (available at www.jneurosci.org as supplemental material), and the results primarily mirrored the results of the regressions with the standardized residuals after correcting for TBV. Additional analyses were therefore not performed with the proportional measures as dependent variables. The regression plots for proportional volumes are shown in supplemental Figure 2 (available at www.jneurosci.org as supplemental material).

Characteristics of the rate of change of nonlinearly developing structures

When age ² was a significant predictor independent of age, nonlinear regression analyses were run. Relative differential nonlinear trajectories were found for the cerebral cortex, cerebral WM, brainstem, and cerebellum WM in the raw volume analyses, and additionally for the amygdala and hippocampus in the analysis with the TBV-corrected volumes. The time constant values are shown in Table 6. Cerebellar WM, amygdala, and hippocampus appeared to undergo a relatively rapid change early in the studied age span, whereas the cerebral WM, cerebral cortex, and brainstem showed less steep changes in the earliest part of the age span. Within the cerebral cortex, the frontal and temporal lobes appeared to approach the peak in volume reduction later than the parietal and occipital lobes, as seen in Table 7. Time constant estimates were derived from the exponential fitting analysis of frontal, parietal, and occipital thickness, showing earlier thinning in the occipital lobes. Only the parietal lobe surface area showed a nonlinear age relationship, making it difficult to compare the time constant estimate with the trajectories of the remaining lobar areas.

Table 6.

TBV-corrected volumes

Raw volumes

R ²

t

R ²

t

Absolute change

Percentage change

Table 7.

TBV-corrected volumes

Raw volumes/variables

R ²

t

R ²

t

Absolute change

Percentage change

Estimated percentage change

From the raw volumes, percentage change from 8 to 30 years was estimated, and the results are shown in Table 6. For the structures with nonlinear age relationships, the nonlinear analysis equation was used to estimate the percentage change. The lateral ventricular volume changed the most, with a near 100% increase during this age range. Also, the WM volumes (in cerebrum, cerebellum, and brainstem) showed high percentage increases in volume. As can be seen in Figure 3, the cerebral cortex, followed by the putamen, accumbens, and pallidum, showed the greatest percentage decrease, whereas the amygdala, hippocampus, and thalamus were the structures that appeared to change the least from 8 to 30 years of age. Within the cerebral cortex, thickness changed by 8.3-19.4%, with the greatest change in parietal and occipital lobes. Surface area changed by 7.0-20.9%, with the greatest change observed in the frontal lobes, whereas lobar volumes changed 15.0-28.5%, with the greatest change observed in parietal lobes, as seen in Table 7.

Differential age relationships within subcortical regions

To explore whether differential age relationships between adjacent subcortical structures can be observed, statistical comparisons were made within the basal ganglia and within the temporal lobe subcortical structures. Within the basal ganglia, the putamen showed a stronger relationship with age than the caudate (TBV-corrected volumes), as there was a significant difference between the age correlations of −0.19 for the caudate and −0.40 for the putamen ( t₍₁₆₈₎ = 2.37; p < 0.05). The difference in age correlations between the caudate and the pallidum (−0.31) was not significant ( t₍₁₆₈₎ = 1.56; p > 0.05). There was no significant difference between the age correlation of 0.26 for the hippocampus and 0.36 for the amygdala ( t₍₁₆₈₎ = 1.29; p > 0.05).

Discussion

The results of the present study confirmed the general notion that GM decreases while WM and lateral ventricular volumes increase during late childhood and adolescence ( Jernigan et al., 1991; Giedd et al., 1996b, 1999; Reiss et al., 1996; Sowell et al., 1999, 2002; Gogtay et al., 2004; Lenroot et al., 2007; Shaw et al., 2008), while simultaneously pointing to important nuances in subcortical development. Volume reductions were not observed in all GM structures; there were no age effects on thalamic volume, and the hippocampus and amygdala showed volume increases. Overall, the results were characterized by substantial heterogeneity. Unless specified otherwise, the remainder of the discussion will deal with the results of the TBV-corrected analyses.

Heterogeneous age effects

The reduction in cortical volume was driven by both a reduction in thickness and a slight reduction in surface area, seen in all lobar regions. The cerebral cortex, and the cortical lobe volumes, differed from subcortical GM volumes by showing a stronger relationship with age (37-77% vs 4-16% explained variance of age; 15-28.5% vs 3-13% change from 8 to 30 years) and by showing a nonlinear relationship with age, whereas linear age relationships were found in most subcortical structures. Proportionally stronger age effects in frontal cortical GM compared with subcortical structures have also been reported by Sowell et al. (2002). Lobar cortical thickness was characterized by strong age effects but less percentage change than cortical volumes [see Tamnes et al. (2009) for voxel-wise and regional thickness analyses]. Cortical surface area showed less developmental effects, comparable to effects seen for subcortical raw and TBV-corrected volumes. Differential properties of thickness and surface area have also been pointed out by Panizzon et al. (2009) using an adult sample.

WM volumes changed more than cortical and subcortical GM, which is in line with previous literature ( Sowell et al., 2002), up to 27%. All WM volumes showed decelerating nonlinear increases during development. However, different rates of development were revealed by looking at the time constants from the nonlinear regression analyses. The increase in WM volume of the brainstem and cerebellum approached the asymptote earlier than what was the case for cerebral WM.

Basal ganglia and the accumbens area

Basal ganglia structures and the accumbens area showed similarities in development as they all were moderately and linearly related to age. These structures changed relatively little compared with cortical and frontal cortical GM. Sowell et al. (2002) have previously pointed out the similarities in development between different basal ganglia structures. However, the differences may also be noteworthy, as there has been a focus on only the caudate in many reports ( Lenroot et al., 2007; Wilke et al., 2007; Giedd, 2008), and few studies, with the exception of Sowell et al. (2002) and Jernigan et al. (1991), have tried to disentangle maturational effects on different parts of the basal ganglia. In the present study, the caudate showed the weakest relationship with age (nonsignificant with Bonferroni correction) and changed the least compared with lenticular nuclei. The statistical analyses of the basal ganglia structures' different relationships with age also confirmed this point. This seems to be in line with previous reports in which both the caudate and lenticular nuclei have been studied in the same sample ( Jernigan et al., 1991; Sowell et al., 2002).

Cerebellum

Differential developmental properties were also discovered for GM and WM within the cerebellum. A general cerebellar volume increase has been observed ( Sowell et al., 2002; Mackie et al., 2007), along with a nonlinear decrease in cerebellar GM volume ( Mackie et al., 2007). Our results confirmed a general tendency of cerebellar GM reduction, although linear. Giedd et al. (2007) commented on the cerebellum as being among the latest structures to reach peak volume. Interestingly, our cerebellar WM analysis indicates an early development, as the time constant derived from the nonlinear equation was low, especially compared with cerebral WM. Although our data could not pinpoint the exact timing of cerebellar GM development (because of the linear fitting of data), the results for cerebellar WM indicate differential developmental trajectories in GM and WM in the cerebellum. This needs to be confirmed by future studies.

Temporal lobe subcortical GM

Hippocampus and amygdala diverge from the other GM volumes in their slight increase with age during development. Our data are in line with some of the previous studies and reviews ( Giedd et al., 1996b; Toga et al., 2006; Guo et al., 2007), but discrepancies exist ( Giedd et al., 1996b; Sowell et al., 2002; Gogtay et al., 2006). It must be noted that the observed age effect in the hippocampus was weak, appearing only when correcting for TBV (without Bonferroni correction), although a tendency was also visible for the raw volumes. Gogtay et al. (2006) argued that the weak and diverging developmental effects in the hippocampus may be attributable to opposing effects in subregions of the hippocampus. However, the relatively more robust effect in the nearby amygdala supports the finding of a volume increase. Caution must be taken, as the contribution of age ² to these two volumes did not reach statistical significance when correcting for multiple comparisons.

GM reduction and WM increase in light of developmental principles

Myelination has been shown to constitute a major developmental principle during late childhood and through adolescence, supported by recent diffusion tensor imaging studies ( Giedd et al., 1999; Paus et al., 2001; Sowell et al., 2002; Giedd, 2004; Barnea-Goraly et al., 2005; Ashtari et al., 2007; Giorgio et al., 2008; Lebel et al., 2008). GM reduction is thought to, in part, reflect synaptic pruning ( Huttenlocher, 1990). However, it is also possible that GM reductions are attributable to increased myelination, pushing the border between segmented GM and WM in favor of WM volume ( Sowell et al., 2004; Shaw et al., 2008). This would likely be the case also for subcortical structures, surrounded by myelinated, long-distance axonal projections of neurons. The temporal lobe structures show a different developmental pattern than the other GM structures, making it possible that other neurobiological mechanisms are responsible for the maturational changes observed. Although the present results do not directly explain neurobiological mechanisms in development, the pattern of increases and decreases observed here represents valuable developmental information and points to the need for a better understanding of the biological mechanisms in brain development.

These developmental principles must be considered together with cognitive development, to disentangle the relationships between the dynamics of volumetric changes and cognitive performance throughout childhood and adolescence. The nonlinear and dynamic nature of brain development makes this especially important, as the differential trajectories may have different cognitive implications. For instance, there is some evidence from functional MRI studies to suggest that subcortical structures and cortical areas may play different roles at different times during cognitive development ( Menon et al., 2005; Ciesielski et al., 2006; Galvan et al., 2006). However, it remains elusive how this relates to differential regional structural neurodevelopment.

Limitations and conclusions

Future investigations are needed to confirm the results in a longitudinal design. This has the advantage of being more sensitive to individual differences in developmental trajectories, thus potentially capturing developmental peaks early in the age span, as has been found by Giedd et al. (1999). Studying children and adults in the same sample also raises some methodological problems. For instance, the scans of younger children may be more prone to movement artifacts, which could lead to erroneous estimations of brain measures. Our data were characterized by a greater reliance on only one scan among the younger participants. However, negligible differences have been found between using one versus two averaged scans ( Han et al., 2006). Children may have smaller neuroanatomical volumes, and hence biases may be introduced when comparing neuroanatomical segmentations across children and adults. For instance, child scan segmentations may suffer from greater partial volume effects. However, the TBV of 8 year olds is probably comparable to that of adults ( Reiss et al., 1996), and the classification technique used employs a registration procedure that is especially well suited to account for varying anatomies ( Fischl et al., 2004). Also, by visual inspection of the segmentations, the techniques used were deemed to yield accurate results in children of the present age. Another limitation of the present study is that brain development was only studied from 8 years of age and up. Clearly, dramatic changes take place much earlier in development ( Giedd et al., 1996a; Gogtay et al., 2004; Gogtay et al., 2006; Knickmeyer et al., 2008). Still, our cross-sectional data from the present age range yielded results that shed new light on brain development seen as a whole. GM decreases in cerebral cortex, basal ganglia/accumbens, and cerebellum were confirmed, as were GM increases in hippocampus and amygdala and WM and ventricular increases. Especially noteworthy are the nuances in developmental differences observed, as shown by the differential age effects within the basal ganglia, between cortex and subcortical structures, and the patterns of cerebellar WM development compared with both cerebellar GM and cerebral WM.

Footnotes

This work was supported by the Norwegian Research Council (177404/W50 and 186092/V50 to K.B.W., 170837/V50 to Ivar Reinvang); the University of Oslo (K.B.W.); and the Department of Psychology, University of Oslo (A.M.F.). We thank Mari Torstveit, Victoria Torp Sells, and Vivi Agnete Larsen for contributing to the data acquisition; Dag Erik Eilertsen for assistance in statistical analyses; and Inge Amlien and Tonje Østby for help with preparation of the figures. We also thank all participants and their parents for taking part in this study.

References

Wechsler D. Wechsler abbreviated scale of intelligence. San Antonio, TX: The Psychological Corporation; 1999. [Google Scholar]

Articles from The Journal of Neuroscience are provided here courtesy of Society for Neuroscience

# data[].attributes.attachment_link.title
Heterogeneity in Subcortical Brain Development: A Structural Magnetic Resonance Imaging Study of Brain Maturation from 8 to 30 Years

# data[].attributes.attachment_link.description
Brain development during late childhood and adolescence is characterized by decreases in gray matter (GM) and increases in white matter (WM) and ventricular volume. The dynamic nature of development across different structures is, however, not well ...

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1. Different papers were assigned to you

2. Please reply the assigned Qs based on your paper

3. The deadline to finish each assignment will be announced

4. Please think independently and search the materials on your own

5. Post your answers as the new POST

6. Past your references as the COMMENTS under your post

# data[].attributes.body
1. Two-Three assignments will be posted based on the assigned paper

2. The deadline to finish each assignment will be announced

3. Please think independently and search the materials on your own

4. Post your answers as the new POST

5. Past your references as the COMMNETS under your post

# data[].attributes.body
1. Two-Three assignments will be posted based on the assigned paper

2. The deadline to finish each assignment will be announced

3. Please think independently and search the materials on your own

4. Post your answers as the new POST

5. Past your references as the COMMNETS under your post

# data[].attributes.body
(The template of your reply post)

A1. Please report the answer that you’ve digested by posting as lots of following tasks will be based on your previous reply.

A2. Feel free to use any figures or any multimedia materials as the answers.

A3. Please site the ref. for the materials you majorly read or use for your current post

# data[].attributes.body
(The template of your reply post)

A1. Please report the answer that you’ve digested by posting as lots of following tasks will be based on your previous reply.

A2. Feel free to use any figures or any multimedia materials as the answers.

A3. Please site the ref. for the materials you majorly read or use for your current post

# data[].attributes.body
1. Please list more than 10 new jargons (the words professional in this field, and you’re not familiar with) in this paper

2. How do you deal with the jargons?

3. Please list the aforementioned jargons that are the structure in the brain

# data[].attributes.body
1. List TWO of the structures in the brain (any brain region/architecture/microstructure …) that you want to know more.

2. Please summarize the major function of these two structures.

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1. Please find out whether these structure can be found in rodents.

2. What are the same and different features for these structure between the human and rodent brain.

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Please describe how these two structures changed or matured in advance from neonates to adults?

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1. Which MRI imaging mentioned in the study can be used to depict the changes in the brain structures you're interested in?

2. Why can the MRI measure the change?

# data[].attributes.body
A1.

Q2: I deal with these jargons by using online neuroscience glossary (WashU Medicine) or google them.

Q3:

Structures in the brain

Human telencephalon

Thalamocortical axon

White matter

Basal ganglia

Cortical gyri and sulci

Corpus callosum

Coronal radiata

Right amygdala

Bilateral entorhinal cortex

Left insula

# data[].attributes.body
I interpret these jargons by seeing if there are any recognizable associations that I already know. For example, I know that any word with “cerebral” is most likely closely associated with the cerebrum and the word “bilateral” most likely means affecting both sides. I also look at the prefixes and suffixes to take a guess on what the jargon means. For example, words ending in “-cation” most likely mean an ongoing process.

Aforementioned jargons as brain structures:

Inferior/superior cerebellar peduncles

Ventrolateral nuclei of the thalamus

Telencephalon

Lentiform nucleus

Coronal radiata

Centrum semiovale

Bilateral entorhinal cortex

Left insula

# data[].attributes.body
1. Amygdala

I recently read a book called Almond by Sohn Won-pyung about a boy with alexithymia, a condition associated with the inability to identify and understand emotions. The boy in the story had a smaller amygdala, which made me wonder what exactly about the amygdala contributes to this condition. The amygdala is part of the limbic system, a structure that acts as the center for emotions. It partly controls one’s attitude towards stress, hunger, and sex. The amygdala itself regulates people’s emotions, especially anger and fear. It is connected to circuits associated with one’s ability to infer intentions of others’ language, gaze, and gestures. Lastly, it also plays a role in memory due to its consolidation of memories that evoke strong emotional responses.

2. Brainstem & Cerebral Cortex

In AP Psychology, I learned about the Activation-Synthesis Theory of dreams, which is a topic in neuroscience that still remains quite undiscovered. The Activation-Synthesis Theory states that the brainstem fires random electrical impulses during REM sleep and the cerebral cortex makes sense of these impulses by spinning them into stories our minds perceive as dreams. Besides its role in forming dreams, the brainstem is a structure that connects the cerebrum to the spinal cord and cerebellum. It contains other structures such as the midbrain, pons, and medulla oblongata. It controls our body’s involuntary processes such as breathing and heart rate. On the other hand, the cerebral cortex serves as an outer layer of nerve tissue with many grooves and crevices. It is responsible for many high-level functions such as memory, learning, emotions, and consciousness.

# data[].attributes.body
A2

- Basal Ganglia

The basal ganglia are a group of structures near the center of the brain, including the striatum, globus pallidus, and subthalamic nucleus. The major function of this structure is movement, decision-making, and a reward system. For motor control, the basal ganglia control the selection and initiation of action through two pathways: direct and indirect. The direct pathway causes motion by inhibiting GPi(Globus Pallidus Internus), which allows the thalamus to excite the motor cortex. The indirect pathway, on the other hand, inhibits motor activity by inhibiting GPe (Globus Pallidus Externus), which excites the subthalamic nucleus and the GPi. Dysfunction in this system leads to movement disorders such as Parkinson’s disease (too little movement) and Huntington’s disease (excessive involuntary movement).

- Myelin sheath + Oligodendrocytes

While watching The Resident, one of the main characters, Dr. Bell, was diagnosed with Multiple Sclerosis. This is a disease that causes the breakdown of the protective covering of nerves, the myelin sheath. Composed of fat and protein, it insulates axons and enables rapid conduction by allowing electrical impulses to jump between nodes of Ranvier. The glial cell that forms this protective layer is known as the oligodendrocyte. In addition to myelination, oligodendrocytes provide metabolic support to neurons by supplying energy substrates, ensuring long-term axonal survival. When oligodendrocytes are destroyed, remyelination often fails, which can lead to chronic neurodegeneration and progressive disability.

# data[].attributes.body
Amygdala: Both rodents and humans have amygdalas. Their structure is similar in their basolateral and centromedial divisions. They have similar functions in threat detection and defense processing. However, humans are better at emotional regulation because of their more developed prefrontal cortexes, which have a role in executive functioning such as planning and decision-making.

Brainstem: Both rodents and humans have brainstems. They have similar functions in regulating involuntary functions such as breathing and heart rate, which keep us alive. However, the brainstems in rodents are much smaller with differences in their neuronal soma shape. The shape of their soma are almost spherical while those of humans come in more complex shapes (triangular, ovoid, granular). Humans also have extended structures within the brainstem such as the nucleus paramedian dorsalis (PMD), which play a role in processing vestibular information

# data[].attributes.body
A3

Basal Ganglia

Both humans and rodents contain this structure in their brains, and their role in motor control is also similar. However, humans have a more complex and clearly defined substructure, like the caudate, putamen, and globus pallidus. Since human brains are larger and more complex, they can perform more complicated behaviors than rodents.

Myelin sheath + Oligodendrocytes

Both humans and rodents contain myelin sheath and oligodendrocytes in their brains. However, due to the difference in brain size, the myelin sheaths in humans are longer, which requires more oligodendrocytes per neuron. In addition, humans and rodents share a majority of myelin proteins, but some are still species-specific.

# data[].attributes.body
Amygdala: The primate amygdala is formed in the gestational period. However, it continues to mature well into adulthood. The process of this maturation include dendritic enlargement, synaptogenesis (formation of synapses that strengthen networks), and gliogenesis (formation of glial cells). Maturation of the amygdala around the perinatal period is also easily influenced by environmental stimuli.

Brainstem: In the gestational period, the brainstem greatly increases in volume and proportion. After birth, myelination occurs most actively in the first two years and continues at a slower rate as the child grows. At the neonatal stage, the functional organization of the brainstem is also localized or “segregated.” Later on as the brainstem matures into adulthood, new neurons form via neurogenesis and networks become more integrated or connected together.

# data[].attributes.body
Basal ganglia

For the basal ganglia, their structural development is largely restricted to embryonic development, which means that the neonatal basal ganglia are structurally similar to the adult basal ganglia. However, the function in the neonatal basal ganglia isn’t fully developed yet, and the motor behaviors are still poorly coordinated. As the function becomes mature, it allows better motor control and procedural learning.

Myelin sheath+oligodendrocytes

The myelin sheath in a neonate has just started to form, which is thin and loosely compacted. As the structure matures, it gets thicker, allowing for fast neural transmission and supporting complex thoughts.

For oligodendrocytes, neonates mostly contain oligodendrocyte progenitor cells (OPCs) and only have a few mature oligodendrocytes, but as they grow older, the mature oligodendrocyte population increases and stabilizes, which helps to form a thick myelin sheath.

# data[].attributes.body
Basal ganglia

Since it is a gray matter structure, a conventional MRI can be used to depict the change. It measures changes by giving a contrast between gray and white matter, making the basal ganglia clearly visible.

Myelin sheath and oligodendrocytes

White matter is more myelinated than the gray matter, so a DTI can be used to depict the change. In addition, the myelin sheath is a lipid layer surrounding the axons, meaning that it can affect water diffusion. DTI can measure the change in water diffusion to show the change in the Myelin sheath and oligodendrocytes.

# data[].attributes.body
To Queenie

https://www.sciencedirect.com/science/article/pii/S187892932100116X

To Eva

https://pmc.ncbi.nlm.nih.gov/articles/PMC6666647/

# data[].attributes.attachment_link.readable_text
Abstract

Brain development during late childhood and adolescence is characterized by decreases in gray matter (GM) and increases in white matter (WM) and ventricular volume. The dynamic nature of development across different structures is, however, not well understood, and the present magnetic resonance imaging study took advantage of a whole-brain segmentation approach to describe the developmental trajectories of 16 neuroanatomical volumes in the same sample of children, adolescents, and young adults ( n = 171; range, 8-30 years). The cerebral cortex, cerebral WM, caudate, putamen, pallidum, accumbens area, hippocampus, amygdala, thalamus, brainstem, cerebellar GM, cerebellar WM, lateral ventricles, inferior lateral ventricles, third ventricle, and fourth ventricle were studied. The cerebral cortex was further analyzed in terms of lobar thickness and surface area. The results revealed substantial heterogeneity in developmental trajectories. GM decreased nonlinearly in the cerebral cortex and linearly in the caudate, putamen, pallidum, accumbens, and cerebellar GM, whereas the amygdala and hippocampus showed slight, nonlinear increases in GM volume. WM increased nonlinearly in both the cerebrum and cerebellum, with an earlier maturation in cerebellar WM. In addition to similarities in developmental trajectories within subcortical regions, our results also point to differences between structures within the same regions: among the basal ganglia, the caudate showed a weaker relationship with age than the putamen and pallidum, and in the cerebellum, differences were found between GM and WM development. These results emphasize the importance of studying a wide range of structural variables in the same sample, for a broader understanding of brain developmental principles.

Introduction

Subcortical structures are important in developmental disorders ( Krain and Castellanos, 2006), and we need a better understanding of the dynamics of differential subcortical brain development. However, few magnetic resonance imaging (MRI) studies cover normal brain development in multiple subcortical regions in childhood and adolescence. The aim of the present study was to describe and compare the developmental trajectories of several subcortical structures in the same sample of participants, thereby shedding light on the dynamic interplay between different brain structures in development.

It is generally acknowledged that gray matter (GM) volume increases in early childhood and decreases during adolescence ( Jernigan et al., 1991; Reiss et al., 1996; Giedd et al., 1999; Sowell et al., 1999, 2002; Lenroot et al., 2007; Wilke et al., 2007; Shaw et al., 2008). This may, in part, reflect early synaptic arborization and later pruning ( Huttenlocher, 1990). Results indicate that subcortical GM structures show differential developmental trajectories ( Giedd et al., 1996b; Sowell et al., 2002; Toga et al., 2006). Because most studies have quantified only one or a few structures, the relative development of the subcortical structures is still unclear, however. For instance, different emphasis has been put on either the caudate or lenticular nuclei in development in various reports, sometimes because of methodological constraints ( Jernigan et al., 1991; Giedd et al., 1996a; Sowell et al., 1999, 2002; Wilke et al., 2007). There has been some focus on structural development of the cerebellum ( Sowell et al., 2002; Liu et al., 2003; Mackie et al., 2007) and accumbens ( Sowell et al., 2002), but cerebellar development is still not characterized in terms of separate WM and GM development. Furthermore, there is a need for a broader understanding of the development of hippocampus and amygdala, as these structures often appear to increase during development, although discrepancies exist ( Giedd et al., 1996b; Sowell et al., 2002; Gogtay et al., 2006; Toga et al., 2006; Guo et al., 2007). It is not clear how this is to be understood within a (proposed) framework of arborization/pruning as general principles of GM development. White matter (WM) volume has been shown to increase, often linearly, probably driven by increased myelination during adolescence and early adulthood ( Reiss et al., 1996; Giedd et al., 1999; Sowell et al., 2002; Lenroot et al., 2007). However, developmental trajectories for WM in the cerebrum versus cerebellum have not been compared.

In the current study, maturational changes were described and compared across volume estimates for the caudate, putamen, pallidum, accumbens area, amygdala, hippocampus, thalamus, cerebellar cortex and WM, brainstem, cerebral cortex and WM, and ventricles (lateral ventricles, inferior lateral ventricles, third ventricle, fourth ventricle), along with volume, thickness, and surface area estimates of the cortical lobes. Especially, it was investigated how subcortical structures develop compared with the cerebral cortex, how the basal ganglia/striatal structures compare in development, how the amygdala and hippocampus develop, and how cerebellar development can be described in terms of separate GM and WM volume.

Materials and Methods

Figure 1.

Regression plots showing the relationship between age and bilateral volumes of thalamus (a), cerebral cortex (b), cerebral WM (c), cerebellum GM (d), brainstem (e), cerebellum WM (f), caudate (g), putamen (h), the accumbens area (i), pallidum (j), amygdala (k), and hippocampus (l), with age on the horizontal axis and TBV-corrected volume in z-scores on the vertical axis. Also displayed are samples of the segmentation (cerebral WM not shown), based on the group mean.

Figure 2.

Regression plots showing the relationship between age and bilateral volumes of the lateral ventricles (a), the inferior lateral ventricles (b), the third ventricles (c), and the fourth ventricles (d), with age on the horizontal axis and TBV-corrected volume in z-scores on the vertical axis, shown together with samples of the segmentation, based on the group mean.

Results

Descriptive analyses

ANOVA with 13 bilateral brain volumes and two hemispheres as within-subjects factors and age as the between-subject factor showed no significant hemisphere × age interaction effects ( F(124,46) = 1.104; p = 0.36). This analysis was repeated for four bilateral regional (cortical lobe) variables, yielding no significant hemisphere × age interaction effects for volume ( F(124,46) = 0.885; p = 0.706), thickness ( F(124,46) = 1.269; p = 0.179), or surface area ( F(124,46) = 1.092; p = 0.375). Thus, the sum of right- and left-hemisphere volumes was used in all analyses. As expected, males had larger TBVs than females as evidenced by an independent-samples t test (males: 1,290,900 mm ³; SD, 87,897; females: 1,152,300 mm ³; SD, 80,731; t₍₁₆₉₎ = −10.75; p = 0.0001). Also, as can be seen from Table 2, the variance was high within the age groups. Analysis of covariance with 16 TBV-corrected brain volumes as the within-subjects factor, sex as the between-subjects factor, and age as a covariate showed no sex × age interaction effects ( F(22,23) = 0.68; p = 0.82) or sex × age × structure effects ( F(182.737,191.043) = 1.141; p = 0.183). This analysis was also performed with the regional cortical variables, revealing no significant sex × age effects in thickness ( F(22,23) = 0.876; p = 0.743), surface area ( F(22,23) = 0.867; p = 0.630), or TBV-corrected volumes ( F(22,23) = 0.852; p = 0.645) or sex × age × structure effects for thickness ( F(53.864,56.312) = 0.963; p = 0.554), surface area ( F(44.832,46.869) = 0.823; p = 743), or TBV-corrected volumes ( F(42.731,44.673) = 0.881; p = 0.660). There may still be sex effects of interest in select structures. However, since no interaction effect of sex and age was shown in this overall analysis, and sex is not the primary focus of the present study, additional analyses were performed on males and females together.

Table 2.

Age groups

8-11 (n = 38)

12-15 (n = 37)

16-19 (n = 36)

20-23 (n = 33)

24-31 (n = 27)

Total (n = 171)

Relationships between age and brain volumes

The volume for each brain structure in each of five age groups is displayed in Table 2. The volume, thickness, and surface area of each cortical lobe and total cerebral cortex are displayed in supplemental Table 1 (available at www.jneurosci.org as supplemental material). The results of the regression analyses with subcortical brain volumes are shown in Tables 3 and 4, whereas Table 5 shows the results of the regression analyses with cortical variables (volume, thickness, and surface area). In the raw volume analyses, all GM volumes, except hippocampus, amygdala, and thalamus, showed a negative relationship with age ( p < 0.05). Amygdala and hippocampus showed a slight positive relationship with age, but only the amygdala increase was significant. The cerebral cortex was the only GM structure that showed a curvilinear relationship with age. This pattern was seen in the parietal and occipital lobe volumes in the regional cortical analysis, whereas the frontal and temporal lobes were best described by linear age relationships. In the cortical thickness analysis, the frontal, parietal, and occipital lobes showed quadratic age relationships, and the temporal lobe showed a linear age relationship. All lobar surface areas showed statistically significant age relationships, and the parietal lobe surface area also showed a quadratic relationship with age ( p < 0.05). All WM volumes (cerebral WM, cerebellar WM, brainstem) showed a quadratic relationship with age ( p < 0.05). The lateral ventricles increased linearly with age, whereas the inferior lateral ventricles and the third and fourth ventricles showed no significant relationship with age. With Bonferroni corrections, the amygdala and caudate age relationships no longer reached significance in the volumetric analysis, whereas the additional contribution of age ² to the explained variance in the parietal surface area disappeared. When introducing age ³ as a third regressor, an additional unique variance ( p < 0.05) was explained in only one structure, the pallidum, for which R ² increased from 0.10 to 0.13 ( p = 0.02) (not displayed in the tables). The regression plots for the raw volumes are displayed in supplemental Figure 1 (available at www.jneurosci.org as supplemental material). The regression plots for the regional cortical variables (thickness and surface area) are displayed in Figure 3.

Table 4.

Regression with age

Regression with age and age²

β

F

p

R ²

F

p

R ²

p age2a

Table 5.

Regression with age

Regression with age and age²

β

F

p

R ²

F

p

R ²

p age2a

Figure 3.

Regression plots showing the relationship between age and bilateral cortical volume (TBV corrected), thickness, and surface area for each cortical lobe and for the total cerebral cortex, with age on the horizontal axes. The measurements are standardized residuals (volumes), in millimeters (thickness), and in square millimeters (surface area) on the vertical axes. The parcellation of the cortical lobes are displayed in the left column.

When the analyses were repeated with the TBV-corrected volumes as the dependent variables ( Tables 4, 5), the finding that most GM volumes decreased with age while the WM volumes increased was replicated. However, hippocampus showed a significant positive relationship with age, and both the hippocampus and the amygdala showed a nonlinear increase in volume. The regional cortical analysis revealed statistically significant contributions of age ² in all four cortical lobes. As in the analyses with the raw volumes, an additional unique variance ( p < 0.05) was explained in only the pallidum when introducing age ³ as a third regressor, with R ² increasing from 0.11 to 0.13 ( p = 0.048) (not displayed in the tables). The amount of variance explained by age tended to increase for the cerebral cortex and WM, cerebellum WM, brainstem, amygdala, putamen, lateral ventricles, and hippocampus when TBV-corrected volumes were analyzed compared with the raw volumes. When the results were Bonferroni corrected, the TBV-corrected caudate age relationship no longer reached statistical significance, and the contribution of age ² to hippocampus, amygdala, and temporal lobe also disappeared, as did the cubic component in the regression with pallidum. Regression analyses were also performed with TBV, age, and age ² in the model, to enable evaluation of these separate components, and the results are shown supplemental Table 2 (available at www.jneurosci.org as supplemental material). Generally, TBV showed a profound effect on individual brain variables. The TBV-corrected cerebral cortex (frontal and parietal lobes more than temporal and occipital lobes), cerebral WM, brainstem, cerebellar WM, and amygdala showed the greatest impact of age in terms of explained variance, whereas the hippocampus, the cerebellum cortex, and the caudate showed the least impact of age in this analysis. The regression plots for TBV-corrected GM and WM volumes and ventricles are displayed in Figures 1 and 2. The regression plots for the TBV-corrected regional cortical volumes are displayed in Figure 3.

The results of the regression analyses with proportional volumes of TBV are displayed in supplemental Table 3 (available at www.jneurosci.org as supplemental material), and the results primarily mirrored the results of the regressions with the standardized residuals after correcting for TBV. Additional analyses were therefore not performed with the proportional measures as dependent variables. The regression plots for proportional volumes are shown in supplemental Figure 2 (available at www.jneurosci.org as supplemental material).

Characteristics of the rate of change of nonlinearly developing structures

When age ² was a significant predictor independent of age, nonlinear regression analyses were run. Relative differential nonlinear trajectories were found for the cerebral cortex, cerebral WM, brainstem, and cerebellum WM in the raw volume analyses, and additionally for the amygdala and hippocampus in the analysis with the TBV-corrected volumes. The time constant values are shown in Table 6. Cerebellar WM, amygdala, and hippocampus appeared to undergo a relatively rapid change early in the studied age span, whereas the cerebral WM, cerebral cortex, and brainstem showed less steep changes in the earliest part of the age span. Within the cerebral cortex, the frontal and temporal lobes appeared to approach the peak in volume reduction later than the parietal and occipital lobes, as seen in Table 7. Time constant estimates were derived from the exponential fitting analysis of frontal, parietal, and occipital thickness, showing earlier thinning in the occipital lobes. Only the parietal lobe surface area showed a nonlinear age relationship, making it difficult to compare the time constant estimate with the trajectories of the remaining lobar areas.

Table 6.

TBV-corrected volumes

Raw volumes

R ²

t

R ²

t

Absolute change

Percentage change

Table 7.

TBV-corrected volumes

Raw volumes/variables

R ²

t

R ²

t

Absolute change

Percentage change

Estimated percentage change

From the raw volumes, percentage change from 8 to 30 years was estimated, and the results are shown in Table 6. For the structures with nonlinear age relationships, the nonlinear analysis equation was used to estimate the percentage change. The lateral ventricular volume changed the most, with a near 100% increase during this age range. Also, the WM volumes (in cerebrum, cerebellum, and brainstem) showed high percentage increases in volume. As can be seen in Figure 3, the cerebral cortex, followed by the putamen, accumbens, and pallidum, showed the greatest percentage decrease, whereas the amygdala, hippocampus, and thalamus were the structures that appeared to change the least from 8 to 30 years of age. Within the cerebral cortex, thickness changed by 8.3-19.4%, with the greatest change in parietal and occipital lobes. Surface area changed by 7.0-20.9%, with the greatest change observed in the frontal lobes, whereas lobar volumes changed 15.0-28.5%, with the greatest change observed in parietal lobes, as seen in Table 7.

Differential age relationships within subcortical regions

To explore whether differential age relationships between adjacent subcortical structures can be observed, statistical comparisons were made within the basal ganglia and within the temporal lobe subcortical structures. Within the basal ganglia, the putamen showed a stronger relationship with age than the caudate (TBV-corrected volumes), as there was a significant difference between the age correlations of −0.19 for the caudate and −0.40 for the putamen ( t₍₁₆₈₎ = 2.37; p < 0.05). The difference in age correlations between the caudate and the pallidum (−0.31) was not significant ( t₍₁₆₈₎ = 1.56; p > 0.05). There was no significant difference between the age correlation of 0.26 for the hippocampus and 0.36 for the amygdala ( t₍₁₆₈₎ = 1.29; p > 0.05).

Discussion

The results of the present study confirmed the general notion that GM decreases while WM and lateral ventricular volumes increase during late childhood and adolescence ( Jernigan et al., 1991; Giedd et al., 1996b, 1999; Reiss et al., 1996; Sowell et al., 1999, 2002; Gogtay et al., 2004; Lenroot et al., 2007; Shaw et al., 2008), while simultaneously pointing to important nuances in subcortical development. Volume reductions were not observed in all GM structures; there were no age effects on thalamic volume, and the hippocampus and amygdala showed volume increases. Overall, the results were characterized by substantial heterogeneity. Unless specified otherwise, the remainder of the discussion will deal with the results of the TBV-corrected analyses.

Heterogeneous age effects

The reduction in cortical volume was driven by both a reduction in thickness and a slight reduction in surface area, seen in all lobar regions. The cerebral cortex, and the cortical lobe volumes, differed from subcortical GM volumes by showing a stronger relationship with age (37-77% vs 4-16% explained variance of age; 15-28.5% vs 3-13% change from 8 to 30 years) and by showing a nonlinear relationship with age, whereas linear age relationships were found in most subcortical structures. Proportionally stronger age effects in frontal cortical GM compared with subcortical structures have also been reported by Sowell et al. (2002). Lobar cortical thickness was characterized by strong age effects but less percentage change than cortical volumes [see Tamnes et al. (2009) for voxel-wise and regional thickness analyses]. Cortical surface area showed less developmental effects, comparable to effects seen for subcortical raw and TBV-corrected volumes. Differential properties of thickness and surface area have also been pointed out by Panizzon et al. (2009) using an adult sample.

WM volumes changed more than cortical and subcortical GM, which is in line with previous literature ( Sowell et al., 2002), up to 27%. All WM volumes showed decelerating nonlinear increases during development. However, different rates of development were revealed by looking at the time constants from the nonlinear regression analyses. The increase in WM volume of the brainstem and cerebellum approached the asymptote earlier than what was the case for cerebral WM.

Basal ganglia and the accumbens area

Basal ganglia structures and the accumbens area showed similarities in development as they all were moderately and linearly related to age. These structures changed relatively little compared with cortical and frontal cortical GM. Sowell et al. (2002) have previously pointed out the similarities in development between different basal ganglia structures. However, the differences may also be noteworthy, as there has been a focus on only the caudate in many reports ( Lenroot et al., 2007; Wilke et al., 2007; Giedd, 2008), and few studies, with the exception of Sowell et al. (2002) and Jernigan et al. (1991), have tried to disentangle maturational effects on different parts of the basal ganglia. In the present study, the caudate showed the weakest relationship with age (nonsignificant with Bonferroni correction) and changed the least compared with lenticular nuclei. The statistical analyses of the basal ganglia structures' different relationships with age also confirmed this point. This seems to be in line with previous reports in which both the caudate and lenticular nuclei have been studied in the same sample ( Jernigan et al., 1991; Sowell et al., 2002).

Cerebellum

Differential developmental properties were also discovered for GM and WM within the cerebellum. A general cerebellar volume increase has been observed ( Sowell et al., 2002; Mackie et al., 2007), along with a nonlinear decrease in cerebellar GM volume ( Mackie et al., 2007). Our results confirmed a general tendency of cerebellar GM reduction, although linear. Giedd et al. (2007) commented on the cerebellum as being among the latest structures to reach peak volume. Interestingly, our cerebellar WM analysis indicates an early development, as the time constant derived from the nonlinear equation was low, especially compared with cerebral WM. Although our data could not pinpoint the exact timing of cerebellar GM development (because of the linear fitting of data), the results for cerebellar WM indicate differential developmental trajectories in GM and WM in the cerebellum. This needs to be confirmed by future studies.

Temporal lobe subcortical GM

Hippocampus and amygdala diverge from the other GM volumes in their slight increase with age during development. Our data are in line with some of the previous studies and reviews ( Giedd et al., 1996b; Toga et al., 2006; Guo et al., 2007), but discrepancies exist ( Giedd et al., 1996b; Sowell et al., 2002; Gogtay et al., 2006). It must be noted that the observed age effect in the hippocampus was weak, appearing only when correcting for TBV (without Bonferroni correction), although a tendency was also visible for the raw volumes. Gogtay et al. (2006) argued that the weak and diverging developmental effects in the hippocampus may be attributable to opposing effects in subregions of the hippocampus. However, the relatively more robust effect in the nearby amygdala supports the finding of a volume increase. Caution must be taken, as the contribution of age ² to these two volumes did not reach statistical significance when correcting for multiple comparisons.

GM reduction and WM increase in light of developmental principles

Myelination has been shown to constitute a major developmental principle during late childhood and through adolescence, supported by recent diffusion tensor imaging studies ( Giedd et al., 1999; Paus et al., 2001; Sowell et al., 2002; Giedd, 2004; Barnea-Goraly et al., 2005; Ashtari et al., 2007; Giorgio et al., 2008; Lebel et al., 2008). GM reduction is thought to, in part, reflect synaptic pruning ( Huttenlocher, 1990). However, it is also possible that GM reductions are attributable to increased myelination, pushing the border between segmented GM and WM in favor of WM volume ( Sowell et al., 2004; Shaw et al., 2008). This would likely be the case also for subcortical structures, surrounded by myelinated, long-distance axonal projections of neurons. The temporal lobe structures show a different developmental pattern than the other GM structures, making it possible that other neurobiological mechanisms are responsible for the maturational changes observed. Although the present results do not directly explain neurobiological mechanisms in development, the pattern of increases and decreases observed here represents valuable developmental information and points to the need for a better understanding of the biological mechanisms in brain development.

These developmental principles must be considered together with cognitive development, to disentangle the relationships between the dynamics of volumetric changes and cognitive performance throughout childhood and adolescence. The nonlinear and dynamic nature of brain development makes this especially important, as the differential trajectories may have different cognitive implications. For instance, there is some evidence from functional MRI studies to suggest that subcortical structures and cortical areas may play different roles at different times during cognitive development ( Menon et al., 2005; Ciesielski et al., 2006; Galvan et al., 2006). However, it remains elusive how this relates to differential regional structural neurodevelopment.

Limitations and conclusions

Future investigations are needed to confirm the results in a longitudinal design. This has the advantage of being more sensitive to individual differences in developmental trajectories, thus potentially capturing developmental peaks early in the age span, as has been found by Giedd et al. (1999). Studying children and adults in the same sample also raises some methodological problems. For instance, the scans of younger children may be more prone to movement artifacts, which could lead to erroneous estimations of brain measures. Our data were characterized by a greater reliance on only one scan among the younger participants. However, negligible differences have been found between using one versus two averaged scans ( Han et al., 2006). Children may have smaller neuroanatomical volumes, and hence biases may be introduced when comparing neuroanatomical segmentations across children and adults. For instance, child scan segmentations may suffer from greater partial volume effects. However, the TBV of 8 year olds is probably comparable to that of adults ( Reiss et al., 1996), and the classification technique used employs a registration procedure that is especially well suited to account for varying anatomies ( Fischl et al., 2004). Also, by visual inspection of the segmentations, the techniques used were deemed to yield accurate results in children of the present age. Another limitation of the present study is that brain development was only studied from 8 years of age and up. Clearly, dramatic changes take place much earlier in development ( Giedd et al., 1996a; Gogtay et al., 2004; Gogtay et al., 2006; Knickmeyer et al., 2008). Still, our cross-sectional data from the present age range yielded results that shed new light on brain development seen as a whole. GM decreases in cerebral cortex, basal ganglia/accumbens, and cerebellum were confirmed, as were GM increases in hippocampus and amygdala and WM and ventricular increases. Especially noteworthy are the nuances in developmental differences observed, as shown by the differential age effects within the basal ganglia, between cortex and subcortical structures, and the patterns of cerebellar WM development compared with both cerebellar GM and cerebral WM.

Footnotes

This work was supported by the Norwegian Research Council (177404/W50 and 186092/V50 to K.B.W., 170837/V50 to Ivar Reinvang); the University of Oslo (K.B.W.); and the Department of Psychology, University of Oslo (A.M.F.). We thank Mari Torstveit, Victoria Torp Sells, and Vivi Agnete Larsen for contributing to the data acquisition; Dag Erik Eilertsen for assistance in statistical analyses; and Inge Amlien and Tonje Østby for help with preparation of the figures. We also thank all participants and their parents for taking part in this study.

References

Wechsler D. Wechsler abbreviated scale of intelligence. San Antonio, TX: The Psychological Corporation; 1999. [Google Scholar]

Articles from The Journal of Neuroscience are provided here courtesy of Society for Neuroscience

# data[].attributes.attachment_link.title
Heterogeneity in Subcortical Brain Development: A Structural Magnetic Resonance Imaging Study of Brain Maturation from 8 to 30 Years

# data[].attributes.attachment_link.description
Brain development during late childhood and adolescence is characterized by decreases in gray matter (GM) and increases in white matter (WM) and ventricular volume. The dynamic nature of development across different structures is, however, not well ...

# data[].attributes.body
To Queenie

https://www.sciencedirect.com/science/article/pii/S187892932100116X

To Eva

https://pmc.ncbi.nlm.nih.gov/articles/PMC6666647/

# data[].attributes.attachment_link.readable_text
Abstract

Brain development during late childhood and adolescence is characterized by decreases in gray matter (GM) and increases in white matter (WM) and ventricular volume. The dynamic nature of development across different structures is, however, not well understood, and the present magnetic resonance imaging study took advantage of a whole-brain segmentation approach to describe the developmental trajectories of 16 neuroanatomical volumes in the same sample of children, adolescents, and young adults ( n = 171; range, 8-30 years). The cerebral cortex, cerebral WM, caudate, putamen, pallidum, accumbens area, hippocampus, amygdala, thalamus, brainstem, cerebellar GM, cerebellar WM, lateral ventricles, inferior lateral ventricles, third ventricle, and fourth ventricle were studied. The cerebral cortex was further analyzed in terms of lobar thickness and surface area. The results revealed substantial heterogeneity in developmental trajectories. GM decreased nonlinearly in the cerebral cortex and linearly in the caudate, putamen, pallidum, accumbens, and cerebellar GM, whereas the amygdala and hippocampus showed slight, nonlinear increases in GM volume. WM increased nonlinearly in both the cerebrum and cerebellum, with an earlier maturation in cerebellar WM. In addition to similarities in developmental trajectories within subcortical regions, our results also point to differences between structures within the same regions: among the basal ganglia, the caudate showed a weaker relationship with age than the putamen and pallidum, and in the cerebellum, differences were found between GM and WM development. These results emphasize the importance of studying a wide range of structural variables in the same sample, for a broader understanding of brain developmental principles.

Introduction

Subcortical structures are important in developmental disorders ( Krain and Castellanos, 2006), and we need a better understanding of the dynamics of differential subcortical brain development. However, few magnetic resonance imaging (MRI) studies cover normal brain development in multiple subcortical regions in childhood and adolescence. The aim of the present study was to describe and compare the developmental trajectories of several subcortical structures in the same sample of participants, thereby shedding light on the dynamic interplay between different brain structures in development.

It is generally acknowledged that gray matter (GM) volume increases in early childhood and decreases during adolescence ( Jernigan et al., 1991; Reiss et al., 1996; Giedd et al., 1999; Sowell et al., 1999, 2002; Lenroot et al., 2007; Wilke et al., 2007; Shaw et al., 2008). This may, in part, reflect early synaptic arborization and later pruning ( Huttenlocher, 1990). Results indicate that subcortical GM structures show differential developmental trajectories ( Giedd et al., 1996b; Sowell et al., 2002; Toga et al., 2006). Because most studies have quantified only one or a few structures, the relative development of the subcortical structures is still unclear, however. For instance, different emphasis has been put on either the caudate or lenticular nuclei in development in various reports, sometimes because of methodological constraints ( Jernigan et al., 1991; Giedd et al., 1996a; Sowell et al., 1999, 2002; Wilke et al., 2007). There has been some focus on structural development of the cerebellum ( Sowell et al., 2002; Liu et al., 2003; Mackie et al., 2007) and accumbens ( Sowell et al., 2002), but cerebellar development is still not characterized in terms of separate WM and GM development. Furthermore, there is a need for a broader understanding of the development of hippocampus and amygdala, as these structures often appear to increase during development, although discrepancies exist ( Giedd et al., 1996b; Sowell et al., 2002; Gogtay et al., 2006; Toga et al., 2006; Guo et al., 2007). It is not clear how this is to be understood within a (proposed) framework of arborization/pruning as general principles of GM development. White matter (WM) volume has been shown to increase, often linearly, probably driven by increased myelination during adolescence and early adulthood ( Reiss et al., 1996; Giedd et al., 1999; Sowell et al., 2002; Lenroot et al., 2007). However, developmental trajectories for WM in the cerebrum versus cerebellum have not been compared.

In the current study, maturational changes were described and compared across volume estimates for the caudate, putamen, pallidum, accumbens area, amygdala, hippocampus, thalamus, cerebellar cortex and WM, brainstem, cerebral cortex and WM, and ventricles (lateral ventricles, inferior lateral ventricles, third ventricle, fourth ventricle), along with volume, thickness, and surface area estimates of the cortical lobes. Especially, it was investigated how subcortical structures develop compared with the cerebral cortex, how the basal ganglia/striatal structures compare in development, how the amygdala and hippocampus develop, and how cerebellar development can be described in terms of separate GM and WM volume.

Materials and Methods

Figure 1.

Regression plots showing the relationship between age and bilateral volumes of thalamus (a), cerebral cortex (b), cerebral WM (c), cerebellum GM (d), brainstem (e), cerebellum WM (f), caudate (g), putamen (h), the accumbens area (i), pallidum (j), amygdala (k), and hippocampus (l), with age on the horizontal axis and TBV-corrected volume in z-scores on the vertical axis. Also displayed are samples of the segmentation (cerebral WM not shown), based on the group mean.

Figure 2.

Regression plots showing the relationship between age and bilateral volumes of the lateral ventricles (a), the inferior lateral ventricles (b), the third ventricles (c), and the fourth ventricles (d), with age on the horizontal axis and TBV-corrected volume in z-scores on the vertical axis, shown together with samples of the segmentation, based on the group mean.

Results

Descriptive analyses

ANOVA with 13 bilateral brain volumes and two hemispheres as within-subjects factors and age as the between-subject factor showed no significant hemisphere × age interaction effects ( F(124,46) = 1.104; p = 0.36). This analysis was repeated for four bilateral regional (cortical lobe) variables, yielding no significant hemisphere × age interaction effects for volume ( F(124,46) = 0.885; p = 0.706), thickness ( F(124,46) = 1.269; p = 0.179), or surface area ( F(124,46) = 1.092; p = 0.375). Thus, the sum of right- and left-hemisphere volumes was used in all analyses. As expected, males had larger TBVs than females as evidenced by an independent-samples t test (males: 1,290,900 mm ³; SD, 87,897; females: 1,152,300 mm ³; SD, 80,731; t₍₁₆₉₎ = −10.75; p = 0.0001). Also, as can be seen from Table 2, the variance was high within the age groups. Analysis of covariance with 16 TBV-corrected brain volumes as the within-subjects factor, sex as the between-subjects factor, and age as a covariate showed no sex × age interaction effects ( F(22,23) = 0.68; p = 0.82) or sex × age × structure effects ( F(182.737,191.043) = 1.141; p = 0.183). This analysis was also performed with the regional cortical variables, revealing no significant sex × age effects in thickness ( F(22,23) = 0.876; p = 0.743), surface area ( F(22,23) = 0.867; p = 0.630), or TBV-corrected volumes ( F(22,23) = 0.852; p = 0.645) or sex × age × structure effects for thickness ( F(53.864,56.312) = 0.963; p = 0.554), surface area ( F(44.832,46.869) = 0.823; p = 743), or TBV-corrected volumes ( F(42.731,44.673) = 0.881; p = 0.660). There may still be sex effects of interest in select structures. However, since no interaction effect of sex and age was shown in this overall analysis, and sex is not the primary focus of the present study, additional analyses were performed on males and females together.

Table 2.

Age groups

8-11 (n = 38)

12-15 (n = 37)

16-19 (n = 36)

20-23 (n = 33)

24-31 (n = 27)

Total (n = 171)

Relationships between age and brain volumes

The volume for each brain structure in each of five age groups is displayed in Table 2. The volume, thickness, and surface area of each cortical lobe and total cerebral cortex are displayed in supplemental Table 1 (available at www.jneurosci.org as supplemental material). The results of the regression analyses with subcortical brain volumes are shown in Tables 3 and 4, whereas Table 5 shows the results of the regression analyses with cortical variables (volume, thickness, and surface area). In the raw volume analyses, all GM volumes, except hippocampus, amygdala, and thalamus, showed a negative relationship with age ( p < 0.05). Amygdala and hippocampus showed a slight positive relationship with age, but only the amygdala increase was significant. The cerebral cortex was the only GM structure that showed a curvilinear relationship with age. This pattern was seen in the parietal and occipital lobe volumes in the regional cortical analysis, whereas the frontal and temporal lobes were best described by linear age relationships. In the cortical thickness analysis, the frontal, parietal, and occipital lobes showed quadratic age relationships, and the temporal lobe showed a linear age relationship. All lobar surface areas showed statistically significant age relationships, and the parietal lobe surface area also showed a quadratic relationship with age ( p < 0.05). All WM volumes (cerebral WM, cerebellar WM, brainstem) showed a quadratic relationship with age ( p < 0.05). The lateral ventricles increased linearly with age, whereas the inferior lateral ventricles and the third and fourth ventricles showed no significant relationship with age. With Bonferroni corrections, the amygdala and caudate age relationships no longer reached significance in the volumetric analysis, whereas the additional contribution of age ² to the explained variance in the parietal surface area disappeared. When introducing age ³ as a third regressor, an additional unique variance ( p < 0.05) was explained in only one structure, the pallidum, for which R ² increased from 0.10 to 0.13 ( p = 0.02) (not displayed in the tables). The regression plots for the raw volumes are displayed in supplemental Figure 1 (available at www.jneurosci.org as supplemental material). The regression plots for the regional cortical variables (thickness and surface area) are displayed in Figure 3.

Table 4.

Regression with age

Regression with age and age²

β

F

p

R ²

F

p

R ²

p age2a

Table 5.

Regression with age

Regression with age and age²

β

F

p

R ²

F

p

R ²

p age2a

Figure 3.

Regression plots showing the relationship between age and bilateral cortical volume (TBV corrected), thickness, and surface area for each cortical lobe and for the total cerebral cortex, with age on the horizontal axes. The measurements are standardized residuals (volumes), in millimeters (thickness), and in square millimeters (surface area) on the vertical axes. The parcellation of the cortical lobes are displayed in the left column.

When the analyses were repeated with the TBV-corrected volumes as the dependent variables ( Tables 4, 5), the finding that most GM volumes decreased with age while the WM volumes increased was replicated. However, hippocampus showed a significant positive relationship with age, and both the hippocampus and the amygdala showed a nonlinear increase in volume. The regional cortical analysis revealed statistically significant contributions of age ² in all four cortical lobes. As in the analyses with the raw volumes, an additional unique variance ( p < 0.05) was explained in only the pallidum when introducing age ³ as a third regressor, with R ² increasing from 0.11 to 0.13 ( p = 0.048) (not displayed in the tables). The amount of variance explained by age tended to increase for the cerebral cortex and WM, cerebellum WM, brainstem, amygdala, putamen, lateral ventricles, and hippocampus when TBV-corrected volumes were analyzed compared with the raw volumes. When the results were Bonferroni corrected, the TBV-corrected caudate age relationship no longer reached statistical significance, and the contribution of age ² to hippocampus, amygdala, and temporal lobe also disappeared, as did the cubic component in the regression with pallidum. Regression analyses were also performed with TBV, age, and age ² in the model, to enable evaluation of these separate components, and the results are shown supplemental Table 2 (available at www.jneurosci.org as supplemental material). Generally, TBV showed a profound effect on individual brain variables. The TBV-corrected cerebral cortex (frontal and parietal lobes more than temporal and occipital lobes), cerebral WM, brainstem, cerebellar WM, and amygdala showed the greatest impact of age in terms of explained variance, whereas the hippocampus, the cerebellum cortex, and the caudate showed the least impact of age in this analysis. The regression plots for TBV-corrected GM and WM volumes and ventricles are displayed in Figures 1 and 2. The regression plots for the TBV-corrected regional cortical volumes are displayed in Figure 3.

The results of the regression analyses with proportional volumes of TBV are displayed in supplemental Table 3 (available at www.jneurosci.org as supplemental material), and the results primarily mirrored the results of the regressions with the standardized residuals after correcting for TBV. Additional analyses were therefore not performed with the proportional measures as dependent variables. The regression plots for proportional volumes are shown in supplemental Figure 2 (available at www.jneurosci.org as supplemental material).

Characteristics of the rate of change of nonlinearly developing structures

When age ² was a significant predictor independent of age, nonlinear regression analyses were run. Relative differential nonlinear trajectories were found for the cerebral cortex, cerebral WM, brainstem, and cerebellum WM in the raw volume analyses, and additionally for the amygdala and hippocampus in the analysis with the TBV-corrected volumes. The time constant values are shown in Table 6. Cerebellar WM, amygdala, and hippocampus appeared to undergo a relatively rapid change early in the studied age span, whereas the cerebral WM, cerebral cortex, and brainstem showed less steep changes in the earliest part of the age span. Within the cerebral cortex, the frontal and temporal lobes appeared to approach the peak in volume reduction later than the parietal and occipital lobes, as seen in Table 7. Time constant estimates were derived from the exponential fitting analysis of frontal, parietal, and occipital thickness, showing earlier thinning in the occipital lobes. Only the parietal lobe surface area showed a nonlinear age relationship, making it difficult to compare the time constant estimate with the trajectories of the remaining lobar areas.

Table 6.

TBV-corrected volumes

Raw volumes

R ²

t

R ²

t

Absolute change

Percentage change

Table 7.

TBV-corrected volumes

Raw volumes/variables

R ²

t

R ²

t

Absolute change

Percentage change

Estimated percentage change

From the raw volumes, percentage change from 8 to 30 years was estimated, and the results are shown in Table 6. For the structures with nonlinear age relationships, the nonlinear analysis equation was used to estimate the percentage change. The lateral ventricular volume changed the most, with a near 100% increase during this age range. Also, the WM volumes (in cerebrum, cerebellum, and brainstem) showed high percentage increases in volume. As can be seen in Figure 3, the cerebral cortex, followed by the putamen, accumbens, and pallidum, showed the greatest percentage decrease, whereas the amygdala, hippocampus, and thalamus were the structures that appeared to change the least from 8 to 30 years of age. Within the cerebral cortex, thickness changed by 8.3-19.4%, with the greatest change in parietal and occipital lobes. Surface area changed by 7.0-20.9%, with the greatest change observed in the frontal lobes, whereas lobar volumes changed 15.0-28.5%, with the greatest change observed in parietal lobes, as seen in Table 7.

Differential age relationships within subcortical regions

To explore whether differential age relationships between adjacent subcortical structures can be observed, statistical comparisons were made within the basal ganglia and within the temporal lobe subcortical structures. Within the basal ganglia, the putamen showed a stronger relationship with age than the caudate (TBV-corrected volumes), as there was a significant difference between the age correlations of −0.19 for the caudate and −0.40 for the putamen ( t₍₁₆₈₎ = 2.37; p < 0.05). The difference in age correlations between the caudate and the pallidum (−0.31) was not significant ( t₍₁₆₈₎ = 1.56; p > 0.05). There was no significant difference between the age correlation of 0.26 for the hippocampus and 0.36 for the amygdala ( t₍₁₆₈₎ = 1.29; p > 0.05).

Discussion

The results of the present study confirmed the general notion that GM decreases while WM and lateral ventricular volumes increase during late childhood and adolescence ( Jernigan et al., 1991; Giedd et al., 1996b, 1999; Reiss et al., 1996; Sowell et al., 1999, 2002; Gogtay et al., 2004; Lenroot et al., 2007; Shaw et al., 2008), while simultaneously pointing to important nuances in subcortical development. Volume reductions were not observed in all GM structures; there were no age effects on thalamic volume, and the hippocampus and amygdala showed volume increases. Overall, the results were characterized by substantial heterogeneity. Unless specified otherwise, the remainder of the discussion will deal with the results of the TBV-corrected analyses.

Heterogeneous age effects

The reduction in cortical volume was driven by both a reduction in thickness and a slight reduction in surface area, seen in all lobar regions. The cerebral cortex, and the cortical lobe volumes, differed from subcortical GM volumes by showing a stronger relationship with age (37-77% vs 4-16% explained variance of age; 15-28.5% vs 3-13% change from 8 to 30 years) and by showing a nonlinear relationship with age, whereas linear age relationships were found in most subcortical structures. Proportionally stronger age effects in frontal cortical GM compared with subcortical structures have also been reported by Sowell et al. (2002). Lobar cortical thickness was characterized by strong age effects but less percentage change than cortical volumes [see Tamnes et al. (2009) for voxel-wise and regional thickness analyses]. Cortical surface area showed less developmental effects, comparable to effects seen for subcortical raw and TBV-corrected volumes. Differential properties of thickness and surface area have also been pointed out by Panizzon et al. (2009) using an adult sample.

WM volumes changed more than cortical and subcortical GM, which is in line with previous literature ( Sowell et al., 2002), up to 27%. All WM volumes showed decelerating nonlinear increases during development. However, different rates of development were revealed by looking at the time constants from the nonlinear regression analyses. The increase in WM volume of the brainstem and cerebellum approached the asymptote earlier than what was the case for cerebral WM.

Basal ganglia and the accumbens area

Basal ganglia structures and the accumbens area showed similarities in development as they all were moderately and linearly related to age. These structures changed relatively little compared with cortical and frontal cortical GM. Sowell et al. (2002) have previously pointed out the similarities in development between different basal ganglia structures. However, the differences may also be noteworthy, as there has been a focus on only the caudate in many reports ( Lenroot et al., 2007; Wilke et al., 2007; Giedd, 2008), and few studies, with the exception of Sowell et al. (2002) and Jernigan et al. (1991), have tried to disentangle maturational effects on different parts of the basal ganglia. In the present study, the caudate showed the weakest relationship with age (nonsignificant with Bonferroni correction) and changed the least compared with lenticular nuclei. The statistical analyses of the basal ganglia structures' different relationships with age also confirmed this point. This seems to be in line with previous reports in which both the caudate and lenticular nuclei have been studied in the same sample ( Jernigan et al., 1991; Sowell et al., 2002).

Cerebellum

Differential developmental properties were also discovered for GM and WM within the cerebellum. A general cerebellar volume increase has been observed ( Sowell et al., 2002; Mackie et al., 2007), along with a nonlinear decrease in cerebellar GM volume ( Mackie et al., 2007). Our results confirmed a general tendency of cerebellar GM reduction, although linear. Giedd et al. (2007) commented on the cerebellum as being among the latest structures to reach peak volume. Interestingly, our cerebellar WM analysis indicates an early development, as the time constant derived from the nonlinear equation was low, especially compared with cerebral WM. Although our data could not pinpoint the exact timing of cerebellar GM development (because of the linear fitting of data), the results for cerebellar WM indicate differential developmental trajectories in GM and WM in the cerebellum. This needs to be confirmed by future studies.

Temporal lobe subcortical GM

Hippocampus and amygdala diverge from the other GM volumes in their slight increase with age during development. Our data are in line with some of the previous studies and reviews ( Giedd et al., 1996b; Toga et al., 2006; Guo et al., 2007), but discrepancies exist ( Giedd et al., 1996b; Sowell et al., 2002; Gogtay et al., 2006). It must be noted that the observed age effect in the hippocampus was weak, appearing only when correcting for TBV (without Bonferroni correction), although a tendency was also visible for the raw volumes. Gogtay et al. (2006) argued that the weak and diverging developmental effects in the hippocampus may be attributable to opposing effects in subregions of the hippocampus. However, the relatively more robust effect in the nearby amygdala supports the finding of a volume increase. Caution must be taken, as the contribution of age ² to these two volumes did not reach statistical significance when correcting for multiple comparisons.

GM reduction and WM increase in light of developmental principles

Myelination has been shown to constitute a major developmental principle during late childhood and through adolescence, supported by recent diffusion tensor imaging studies ( Giedd et al., 1999; Paus et al., 2001; Sowell et al., 2002; Giedd, 2004; Barnea-Goraly et al., 2005; Ashtari et al., 2007; Giorgio et al., 2008; Lebel et al., 2008). GM reduction is thought to, in part, reflect synaptic pruning ( Huttenlocher, 1990). However, it is also possible that GM reductions are attributable to increased myelination, pushing the border between segmented GM and WM in favor of WM volume ( Sowell et al., 2004; Shaw et al., 2008). This would likely be the case also for subcortical structures, surrounded by myelinated, long-distance axonal projections of neurons. The temporal lobe structures show a different developmental pattern than the other GM structures, making it possible that other neurobiological mechanisms are responsible for the maturational changes observed. Although the present results do not directly explain neurobiological mechanisms in development, the pattern of increases and decreases observed here represents valuable developmental information and points to the need for a better understanding of the biological mechanisms in brain development.

These developmental principles must be considered together with cognitive development, to disentangle the relationships between the dynamics of volumetric changes and cognitive performance throughout childhood and adolescence. The nonlinear and dynamic nature of brain development makes this especially important, as the differential trajectories may have different cognitive implications. For instance, there is some evidence from functional MRI studies to suggest that subcortical structures and cortical areas may play different roles at different times during cognitive development ( Menon et al., 2005; Ciesielski et al., 2006; Galvan et al., 2006). However, it remains elusive how this relates to differential regional structural neurodevelopment.

Limitations and conclusions

Future investigations are needed to confirm the results in a longitudinal design. This has the advantage of being more sensitive to individual differences in developmental trajectories, thus potentially capturing developmental peaks early in the age span, as has been found by Giedd et al. (1999). Studying children and adults in the same sample also raises some methodological problems. For instance, the scans of younger children may be more prone to movement artifacts, which could lead to erroneous estimations of brain measures. Our data were characterized by a greater reliance on only one scan among the younger participants. However, negligible differences have been found between using one versus two averaged scans ( Han et al., 2006). Children may have smaller neuroanatomical volumes, and hence biases may be introduced when comparing neuroanatomical segmentations across children and adults. For instance, child scan segmentations may suffer from greater partial volume effects. However, the TBV of 8 year olds is probably comparable to that of adults ( Reiss et al., 1996), and the classification technique used employs a registration procedure that is especially well suited to account for varying anatomies ( Fischl et al., 2004). Also, by visual inspection of the segmentations, the techniques used were deemed to yield accurate results in children of the present age. Another limitation of the present study is that brain development was only studied from 8 years of age and up. Clearly, dramatic changes take place much earlier in development ( Giedd et al., 1996a; Gogtay et al., 2004; Gogtay et al., 2006; Knickmeyer et al., 2008). Still, our cross-sectional data from the present age range yielded results that shed new light on brain development seen as a whole. GM decreases in cerebral cortex, basal ganglia/accumbens, and cerebellum were confirmed, as were GM increases in hippocampus and amygdala and WM and ventricular increases. Especially noteworthy are the nuances in developmental differences observed, as shown by the differential age effects within the basal ganglia, between cortex and subcortical structures, and the patterns of cerebellar WM development compared with both cerebellar GM and cerebral WM.

Footnotes

This work was supported by the Norwegian Research Council (177404/W50 and 186092/V50 to K.B.W., 170837/V50 to Ivar Reinvang); the University of Oslo (K.B.W.); and the Department of Psychology, University of Oslo (A.M.F.). We thank Mari Torstveit, Victoria Torp Sells, and Vivi Agnete Larsen for contributing to the data acquisition; Dag Erik Eilertsen for assistance in statistical analyses; and Inge Amlien and Tonje Østby for help with preparation of the figures. We also thank all participants and their parents for taking part in this study.

References

Wechsler D. Wechsler abbreviated scale of intelligence. San Antonio, TX: The Psychological Corporation; 1999. [Google Scholar]

Articles from The Journal of Neuroscience are provided here courtesy of Society for Neuroscience

# data[].attributes.attachment_link.title
Heterogeneity in Subcortical Brain Development: A Structural Magnetic Resonance Imaging Study of Brain Maturation from 8 to 30 Years

# data[].attributes.attachment_link.description
Brain development during late childhood and adolescence is characterized by decreases in gray matter (GM) and increases in white matter (WM) and ventricular volume. The dynamic nature of development across different structures is, however, not well ...

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1. Different papers were assigned to you

2. Please reply the assigned Qs based on your paper

3. The deadline to finish each assignment will be announced

4. Please think independently and search the materials on your own

5. Post your answers as the new POST

6. Past your references as the COMMNETS under your post

# data[].attributes.body
1. The major questions to probe in the study

2. The study aims

3. The major method to prove the questions.

# data[].attributes.body
Amygdala: Functional changes in the amygdala are often measured using fMRI, which can track blood flow and brain activity. Meanwhile, structural changes in the amygdala can be measured through T1/T2-weighted imaging (for gray/white matter) or Diffusion Tensor Imaging (for water movement along white matter tracts).

Brainstem: T1-weight and T2-weighted images are often used to measure changes in the brainstem. For example, if lesions appear on the brainstem such as in patients with multiple sclerosis (MS), using T1-weighted images can help identify areas of tissue loss or atrophy, which appear as dark spots.

# data[].attributes.body
1. The major questions to probe in the study

How do the volumes of various subcortical and cortical brain structures change from childhood through young adulthood?

2. The study aims

Describe and compare the developmental trajectories of several subcortical structures. (The cerebral cortex, cerebral WM, caudate, putamen, pallidum, accumbens area, hippocampus, amygdala, thalamus, brainstem, cerebellar GM, cerebellar WM, lateral ventricles, inferior lateral ventricles, third ventricle, and fourth ventricle)

3. The major method to prove the questions.

Recruited one hundred seventy-one children and young adults (87 females and 84 males).

Data were collected through a 1.5 tesla Siemens Avanto scanner with three-dimensional T1-weighted scans.

Datasets were processed and analyzed at the Neuroimaging Analysis Lab, Center for the Study of Human Cognition, University of Oslo.

Regression plots showing the relationship between age and bilateral volumes of each subcortical and cortical structure.

# data[].attributes.headline
Assignment reply from Queenie (due 10/26)

# data[].attributes.subject
Assignment reply from Queenie (due 10/26)

# data[].attributes.body
The major questions to probe in the study:

How does the amygdala develop from childhood to adolescence? What are the most reliable and accurate techniques to study amygdala development?

The study aims:

Examine how different tracing methods lead to trajectory differences in amygdala development by comparing the segmentation processes of amygdala volumes extracted manually from FreeSurfer, volBrain, and FSL-FIRST

The major method to prove the questions:

Combined cross-sectional and longitudinal methods

Participants did not have any neurological/mental health problems and were not on psychotropic medication

Included data from 201 typically developing controls (TDCs) from 6-17 years old (invited to participate in 3 consecutive waves of data collection at an interval of approximately 1.25 years)

Conducted a T1-weighted MRI at each interval

Included a final sample of 427 samples from 198 participants (105 females and 93 males)

Compared amygdala volumes taken from the three previously mentioned methods

# data[].attributes.body
1. Different papers were assigned to you

2. Please reply the assigned Qs based on your paper

3. The deadline to finish each assignment will be announced

4. Please think independently and search the materials on your own

5. Post your answers as the new POST

6. Past your references as the COMMNETS under your post

# data[].attributes.body
1. One sentence for the major finding of this research

2. Pick up at least FIVE most important figures or tables to support their main findings

3. Explain the results of each selected figure using YOUR OWN wordings. DO NOT copy/paste the statements in Results or Figure captions.

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Major finding: The study found that different structures follow different developmental trajectories, with gray matter generally decreasing and white matter increasing.

Figure 1

The plots show that most gray-matter structures, like the cortex and pallidum, gradually decrease in size as age increases. In contrast, white matter structures, such as cerebral WM, increase in size with age.

Table 2

The table lists the volume of each structure for different age groups. The pattern shows that gray-matter regions, such as the cortex, get smaller, while white-matter regions grow larger.

Figure 3

The figure shows that all four lobes’ volume, thickness, and surface area decrease over time at a slightly different rate.

Table 6

The table quantifies how much each structure changes over time. The column about the percentage change of the raw volume shows that cerebellum GM has a negative value(-8.9), while cerebellum WM has a positive value (27.3). This suggests that the volume for gray-matter structures decreases, while the volume for white-matter structures increases.

Table 7

The table highlights that different lobes change at different rates. The parietal lobe shows the fastest decrease in volume (-28.5%), while the temporal lobe shows the slowest (-15.0%).

# data[].attributes.body
Major finding:

Different methods of amygdala-segmentation tools produce size-dependent biases in children and adolescents, which leads to inconsistent developmental trajectories. Only manual tracing and volBrain after GMV-adjustment reveals the true pattern of amygdala growth across school-age years.

Figure 1:

FreeSurfer over-covers tissue (over-segmentation). VolBrain marks a smaller region (under-segmentation). FSL results in a better shape, but mismatched edges still remain. These differences confirm that different tools in amygdala segmentation generate different volumes relative to manual tracing.

Table 3:

Again, FreeSurfer estimates larger volumes. VolBrain estimates smaller volumes. FSL has inconsistent performance, so this data does not reliably reflect its individual difference. None of these methods have correlations big enough to substitute for manual tracing.

Figure 2:

VolBrain has lower false positive rates and the highest false negative rates, meaning it rarely adds wrong tissue but undercovers actual amygdala tissue. FreeSurfer has higher false positive rates, meaning it overcovers amygdala tissue. FSL is, again, inconsistent across all charts (overlap, false-positive, and false-negative.

Figure 4:

Manual tracing produces a steady, linear increase in amygdala volume as age increases, similar to volBrain. FreeSurfer shows flattened curves that don’t resemble true growth. FSL shows a concave down shape.

Figure 6:

After adjusting amygdala volume by gray matter volume (GMV), the results for volBrain start to resemble results produced by manual tracing (steady and linear). However, after adjusting by intracranial volume (ICV), the results don’t resemble manual tracing.

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(most of the answers should be from the Discussion section of the paper)

1. Please list two findings in this paper showing SIMILAR results with any published study (cite the references).

2. Please list two findings in this paper showing DIFFERENT results from any published study (cite the references).

3. Please list two most important LIMITATIONS of the study.

4. Do you think the results of the paper FULLY answer the aim of the study? Why or why not?