Population-specific brain charts reveal Chinese-Western differences in neurodevelopmental trajectories

Abstract

Human brain charts provide unprecedented opportunities for decoding neurodevelopmental milestones and establishing clinical benchmarks for precision brain medicine ^1-7. However, current lifespan brain charts are primarily derived from European and North American cohorts, with Asian populations severely underrepresented. Here, we present the first population-specific brain charts for China, developed through the Chinese Lifespan Brain Mapping Consortium (Phase I) using neuroimaging data from 43,037 participants (aged 0-100 years) across 384 sites nationwide. We establish the lifespan normative trajectories for 296 structural brain phenotypes, encompassing global, subcortical, and cortical measures. Cross-population comparisons with Western brain charts (based on data from 56,339 participants aged 0-100 years) reveal distinct neurodevelopmental patterns in the Chinese population, including prolonged cortical and subcortical maturation, accelerated cerebellar growth, and earlier development of sensorimotor regions relative to paralimbic regions. Crucially, these Chinese-specific charts outperform Western-derived models in predicting healthy brain phenotypes and detecting pathological deviations in Chinese clinical cohorts. These findings highlight the urgent need for diverse, population-representative brain charts to advance equitable precision neuroscience and improve clinical validity across populations.

Keywords: Chinese population; MRI; brain chart; normative model.

Figure 1.. Structural neuroimaging data of Chinese and Western populations.

a, Phase I dataset of the Chinese Lifespan Brain Mapping (C-LBM) Consortium, comprising neuroimaging data collected across 29 provinces in China. The right panel shows the number of participants and scanning sites within each province. b, Quality-controlled Chinese lifespan neuroimaging dataset derived from 384 scanning sites, comprising 43,037 healthy participants aged 0–100 years. c, Western neuroimaging data aggregated from eight countries: the United States, the United Kingdom, Canada, Ireland, the Netherlands, Belgium, Switzerland, and Australia. d, Quality-controlled Western lifespan neuroimaging dataset derived from 174 scanning sites, including 56,339 healthy participants aged 0–100 years. Box plots depict the age distribution of participants at each site. Boxes represent the interquartile range (25th–75th percentiles), with the median indicated by a horizontal line; whiskers extend to 1.5 times the interquartile range, and points beyond are plotted as outliers. e, Structural brain phenotypes used for normative model charts, including total brain phenotypes (n = 12), subcortical regional phenotypes (n = 12), and cortical regional phenotypes (n = 272). subs, subjects; yr, year.

Figure 2.. Population-specific growth patterns of global brain phenotypes.

Growth patterns of the total intracranial volume (a), cerebral volume (b), cerebellar volume (c), cortical GMV (d), cerebellar GMV (e), subcortical GMV (f), cerebral WMV (g), cerebellar WMV (h), and ventricular volume (i). The panels showed the normative growth curves and growth rates across the lifespan for the Chinese and Western populations. In the growth curve plots, solid lines represent the median (50th percentile), and dotted lines indicate the 5th and 95th percentiles. Growth rates are calculated from the first derivative of the median curves, and 95% confidence intervals (dotted lines) are estimated via 1,000 bootstrap samples (see the Methods for details). Data distributions of these global phenotypes are shown in Supplementary Fig. 2a. The right panels depict differences in the peak age between Chinese and Western models. To quantify differences in the peak age, we performed permutation testing (1,000 iterations) on the pooled data from all Chinese and Western participants, generating a null distribution of peak age differences for each phenotype (see the Methods). GMV, grey matter volume; WMV, white matter volume; CI, confidence intervals; yr, year.

Figure 3.. Population-specific growth patterns of subcortical regional phenotypes.

a, Growth patterns for the GMV of the caudate (a), putamen (b), pallidum (c), thalamus (d), amygdala (e), and hippocampus (f) in the left hemisphere. The panels show the normative growth curves and growth rates across the lifespan for the Chinese and Western populations. In the growth curve plots, solid lines represent the median (50th percentile), and dotted lines indicate the 5th and 95th percentiles. Growth rates are calculated from the first derivative of the median curves, and 95% confidence intervals (dotted lines) are estimated via 1,000 bootstrap samples (see the Methods for details). Data distributions of these subcortical phenotypes are shown in Supplementary Fig. 2b. The right panels depict differences in the peak age between Chinese and Western growth trajectories. To quantify differences in the peak age, we performed permutation testing (1,000 iterations) on the pooled data from all Chinese and Western participants, generating a null distribution of peak age differences for each phenotype (see the Methods for details). g, The peak age map of Chinese and Western populations, and the peak age difference map between these two populations. Corresponding results for the right hemisphere are shown in Supplementary Fig. 3. GMV, grey matter volume; CI, confidence intervals; yr, year.

Figure 4.. Population-specific growth patterns of cortical regional phenotypes.

From top to bottom, the panels correspond to the regional cortical thickness (a), GMV (b), folding index (c), and surface area (d). This figure displays: (1) peak maturation ages across 68 cortical regions in Chinese- and Western-specific normative growth curves and their spatial correlations, and (2) regional differences in peak maturation timing between Chinese and Western populations. GMV, grey matter volume.

Figure 5.. Evaluation of the out-of-sample predictive phenotypic accuracy of Chinese healthy individuals across normative models.

a, Framework for assessing the predictive accuracy (${\mathrm{R}}^2$) via independent Chinese testing data. b, Age and sex distributions of the out-of-sample Chinese participants (testing set). c, Comparisons of the predictive accuracy between the Chinese and Western models across all 296 structural brain phenotypes. d, Percent improvement in the predictive accuracy of Chinese model than Western model. To avoid spurious values resulting from near-zero ${\mathrm{R}}^2$ values in certain phenotypes, improvement rates were computed only for phenotypes with ${\mathrm{R}}^2>0.1$. The numbers of phenotypes with ${\mathrm{R}}^2\ge 0.1$ were 253 and 249 in the Chinese and Western models, respectively. The region numbers shown after each brain phenotype correspond to those in Fig. 1e. GMV, grey matter volume.

Figure 6.. Misestimation of deviation scores in AD by the Western model relative to the Chinese model.

a, Top panel: Individual-level deviation z scores across 296 phenotypes for 399 AD patients, estimated using the Chinese normative model. Bottom panel: Mean deviation z scores for each phenotype across all AD patients. b, Visualization of the mean deviation scores for global, subcortical, and cortical regional phenotypes. The region numbers for the global brain phenotypes correspond to those shown in Fig. 1e. c, Corresponding results derived from the Western normative model. d, Scatter plot comparing the mean deviation scores between the Chinese and Western models across all phenotypes. e, The Chinese model identified a significantly greater proportion of extreme deviations (∣z∣ > 2.6) than did the Western model across AD patients. f, For phenotypes with ≥ 20 patients exhibiting extreme deviations in either model, we assessed whether the Western model systematically over- or underestimated the deviation scores. For the cortical GMV and cortical thickness, all group-level mean deviations were negative; thus, negative Cohen’s d values indicate underestimation of the extreme deviation magnitude, whereas positive values reflect overestimation. GMV, grey matter volume.