Nature pays special attention | full-age brain map release - brain map imaging in hand, I have the brain password

By | November Over the past few decades, neuroimaging has become a ubiquitous tool in basic and clinical research on the human brain
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But there is no very standard quantitative way to standardize neuroimaging
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Therefore, there is an urgent need for an atlas that can integrate a variety of human brain differential characteristic parameters and neuroimaging
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 On April 6, 2022, the RAI Bethlehem research group at the University of Cambridge, UK, and the J.
Seidlitz research group at the University of Pennsylvania, USA jointly published the article Brain charts for the human lifespan in Nature, bringing together an interactive open resource called Brain Charts (Brain charts).
Charts), to standardize current or potential future MRI data samples (http:// on 123,984 MRI scans of participants of all ages from 115 days to 100 years after conception Perform a variety of data quantifications to uncover nonlinear trajectories and rates of change in brain structure, define neurodevelopmental milestones, and reveal neurological and psychiatric outcomes through standardized measurements of patients' atypical brain structures Patterns of neuroanatomical variation in disease, facilitating standard quantification of individual differences in brain maps
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 The growth chart framework for age-related changes was first published in the late 18th century and remains a cornerstone of pediatric care today
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However, more than two hundred years later, the limitations of this growth chart framework have gradually become prominent.
For example, this growth chart only contains a small part of the anthropometric parameters of height, weight, head and tail, etc.
, and it is only applicable to before the age of ten children
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The human brain goes through a long and complex maturation process from pregnancy to about 30 years old, and gradually ages from about 60 years old
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Therefore, the establishment of a standardized assessment tool for brain development and aging is closely related to the research of healthy aging and mental illness, and can also help people realize that the root cause of mental illness is the result of atypical brain development, and the impact of diseases and patients.
families have a better understanding
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However, it is not easy to form such a standard.
For decades, there is no standard for neuroimaging research, and it is difficult to summarize the human brain at different developmental stages
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To this end, the authors propose several possibilities for enabling brain map generation to 1) robustly perform sex-stratified and age-related normative processes in MRI maps; and 2) identify previously unreported more granular milestones of brain development events; 3) improve sensitivity to detect genetic and early-life environment effects on brain structure; and 4) provide standardized effects and atypical neuroanatomical structures collected in a variety of clinical diseases
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This is not the point, but a new beginning
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 First, the authors calculated the generalized additive models for location, scale and shape (GAMLSS) [1-2] on multiple NMR databases (Fig.
1), which can be used for all ages.
Nonlinear correlation trends are optimized and studied
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Specifically, the authors used the GAMLSS model to fit the structural MRI data of the four major brain tissues of the control subjects to the total cortical gray matter volume, total white matter volume, total subcortical gray matter volume, and total ventricular cerebrospinal fluid volume
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This brain map creation process also incorporates image quality control, expert visual curation and image quality metrics, stability and robustness of simulations, phenotypic validation of non-imaging metrics, complementary research, and more
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It was found that total cortical gray matter volume began to increase strongly in the second trimester, peaked at around 6 years of age, and then showed a linear downward trend, an age group identified 2-3 years later than previous studies, with a more stringent The growth trend of total cortical gray matter was characterized by the method
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Total white matter volume grows rapidly from the second trimester to early childhood, peaking at age 28.
7 years and beginning to decline after age 50
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Figure 1.
Multiple MRI databases used in the brain map database.
Further, the authors extend the neuroimaging phenotype and generalize the same GAMLSS modeling approach to assess the structural phenotype of the brain in MRI
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These phenotypes include whole-brain scales, morphological features of the volume of each of the 34 cortical regions, and more
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The authors' results found that total brain surface area was closely related to total brain volume development throughout the lifespan, with both measures peaking around 11-12 years of age
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Using the constructed MRI brain maps, the authors define milestones in human brain development.
Of the total tissue volume, only the total tissue cortical gray matter volume begins to peak before puberty, while the subcortical total gray matter volume begins to peak at Peaks are reached in mid-adolescence and total white matter volume peaks during adolescence
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The growth rate of total tissue cortical gray matter volume peaks in infancy and early childhood
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The total volume of cerebrospinal fluid in the ventricle peaked between the maximum rates of increase in total gray matter and total white matter volume
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In addition, the authors define the inflection points of total gray matter and total white matter differentiation, which may reflect changes in myelination and synaptic hyperplasia
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The acquisition of brain maps also revealed characteristics of motor abilities and other typical periods of brain development
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 The authors' work quantifies and stratifies percentile data according to data such as gender and age, providing more convenient quantitative indicators, and providing a basis for longitudinal comparison of different data and quantitative output of future MRI results
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In addition, the authors analyzed brain MRI maps of various neurological diseases, further enriching the data analysis of atypical brain structures in diseases
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Fig.
2 Working model In general, this work constructs MRI maps of different genders and different ages by large-scale aggregation of brain MRI data of all age groups and modeling using GAMLSS method: brain map (Fig.
2)
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The construction of brain maps provides strong evidence for quantitative research on brain neurodevelopment, and has great application prospects
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At the same time, this open resource and tools that can be applied online also provide immediate opportunities for the study of clinical atypical brain structures
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 Original link: https://doi.
org/10.
1038/s41586-022-04554-y Reference 1.
Stasinopoulos, D.
& Rigby, R.
Generalized additive models for location scale and shape (GAMLSS) in RJ Stat.
Softw.
23, 1–46 (2007) 2.
24.
Borghi, E.
et al.
Construction of the World Health Organization child growth standards: selection of methods for attained growth curves.
Stat.
Med.
25, 247–265 (2006).
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