Written by: Huang Ying

Editor in charge ︱ Wang Sizhen

Editor︱Yang Binwei

Human brain development is a complex and long-lasting process

On August 8, 2022, the research group of Professor Gang Li from the University of North Carolina at Chapel Hill published a paper entitled "Mapping developmental regionalization and patterns of cortical surface area from the Proceedings of the National Academy of Sciences (PNAS)".

In this study, the researchers used a data-driven non-negative matrix factorization method to naturally divide the calculated cortical area of ​​an individual into different regions, so that points located in the same region have similar developmental patterns, while points located in different regions showed relatively differentiated developmental patterns

The researchers found the optimal region partition with 18 partition numbers according to the silhouette coefficients and reconstruction errors of different numbers of partitions (Fig.

Fig.

(Image source: Huang Y, et al.

Then, in order to explore the displayed developmental patterns of cerebral cortical surface area in different regions, the researchers used generalized additive mixed models to fit the developmental curve of cerebral cortical surface area in each region (Figure 2 shows the left hemisphere cortical surface area).

Figure 2.
Developmental trajectories of cortical surface area in different cortical regions of the left hemisphere
.

(Image source: Huang Y, et al.
, PNAS, 2022)

In order to further analyze and compare the developmental pace of cortical surface area between different regions, we normalized each fitted curve with the fitted value at term birth (40 gestational weeks) (Fig.
3 AB).
)
.

Then, the change from birth to 24 months was calculated for each normalized curve, and the changes of all normalized curves were clustered, resulting in three categories with different developmental paces (Fig.
3C)
.

Category 1 (developmental areas 1-3, blue in Figure 3C): all areas of its cortical surface area develop at a slower pace than the entire cortical surface area; Category 2 (developmental areas 4-15, white areas in Figure 3C): all areas of its cortex The pace of surface area development was similar to that of the entire cerebral cortex; category 3 (developmental regions 16-18, red area in Fig.
3C): all its regions developed at a faster pace than the entire cortical surface area
.

Finally, the researchers used stratified bootstrap to perform 1000 resampling of longitudinal data to verify the above results (P < 1e-16, adjusted for multiple comparisons)
.

Figure 3.
Normalized developmental trajectories and developmental categories for different cortical regions
.

(Image source: Huang Y, et al.
, PNAS, 2022)

Conclusions and Discussions, Inspirations, and Prospects In summary, the researchers mapped the first-ever infant brain-based mapping of the Regional division of cortical surface area development and revealed unique developmental patterns for each partition
.

The regional division based on the development of cerebral cortex surface area is different from the existing cortical area division, which is usually based on the characteristics of sulci and gyrus or functional connectivity characteristics, and does not fully utilize the rich information of cortical development; this regional division is also different from the development of cortical thickness.
The regional division shows that cortical surface area and cortical thickness do have their own unique developmental patterns and mechanisms, and the regional divisions obtained based on the developmental patterns of cortical surface area can better reflect the spatiotemporal heterogeneity of cortical area expansion
.

There are some deficiencies in this study
.

First, merging infant brain images from different scanners and different imaging protocols may have an impact on the results
.

To reduce the impact, the researchers used advanced processing tools specific to infant brain images and cortical surface area to reveal the regionalization of cortical development
.

Compared to other cortical properties such as cortical thickness, cortical surface area is relatively robust and insensitive to imaging protocols
.

Furthermore, from the overlapping age ranges of the two datasets, it can also be seen that their developmental trajectories are very similar
.

Therefore, the results of this study on the developmental regionalization of cortical surface area are more accurate and reliable
.

Second, the regional divisions obtained in this study are based only on single cortical properties
.

Since different cortical properties have their own unique neurobiological developmental mechanisms, a single cortical property obviously cannot fully characterize the regionalization of cortical development
.

Based on the above two points, the harmonization of cortical properties from multiple datasets and the use of multiple different cortical properties to jointly characterize cortical developmental regionalization are required in the future
.

In conclusion, this study greatly fills the gap in the previous understanding of early cerebral cortical development patterns, and provides an important reference for exploring and understanding dynamic early brain development in health and disease
.

Original link: https://doi.
org/10.
1073/pnas.
2121748119

Ying Huang, a doctoral student jointly trained by Northwestern Polytechnical University and the University of North Carolina at Chapel Hill, is the first author of this article.
The Department of Radiology and Biomedical Research Imaging Center of the University of North Carolina at Chapel Hill Professor Gang Li from Biomedical Research Imaging Center is the corresponding author of this paper
.

This research was supported by a project grant from the National Institutes of Health (NIH)
.

Research group website: https://bbm.
web.
unc.
edu/
.

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References (swipe up and down to read) 

[1] AE Lyall et al.
, Dynamic development of regional cortical thickness and surface area in early childhood.
Cerebral cortex 25, 2204-2212 (2015).

[2] KE Garcia et al.
, Dynamic patterns of cortical expansion during folding of the preterm human brain.
Proceedings of the National Academy of Sciences 115, 3156-3161 (2018).

[3] F.
Wang et al.
, Developmental topography of cortical thickness during infancy.
Proceedings of the National Academy of Sciences 116, 15855-15860 (2019).

[4] AB Storsve et al.
, Differential longitudinal changes in cortical thickness, surface area and volume across the adult life span: regions of accelerating and decelerating change.
Journal of Neuroscience 34, 8488-8498 (2014).

[5] C.
-H.
Chen et al.
, Genetic topography of brain morphology.
Proceedings of the National Academy of Sciences 110, 17089-17094 (2013).

[6] G.
Li et al.
, Cortical thickness and surface area in neonates at high risk for schizophrenia.
Brain Structure and Function 221, 447-461 (2016).

[7] HC Hazlett et al.
, Early brain overgrowth in autism associated with an increase in cortical surface area before age 2 years.
Archives of general psychiatry 68, 467-476 (2011)

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