Longitudinal development of the human white matter structural connectome and its association with brain transcriptomic and cellular architecture

From childhood to adolescence, the spatiotemporal development pattern of the human brain white matter connectome and its underlying transcriptomic and cellular mechanisms remain largely unknown. With a longitudinal diffusion MRI cohort of 604 participants, we map the developmental trajectory of the white matter connectome from global to regional levels and identify that most brain network properties followed a linear developmental trajectory. Importantly, connectome-transcriptomic analysis reveals that the spatial development pattern of white matter connectome is potentially regulated by the transcriptomic architecture, with positively correlated genes involve in ion transport- and development-related pathways expressed in excitatory and inhibitory neurons, and negatively correlated genes enriches in synapse- and development-related pathways expressed in astrocytes, inhibitory neurons and microglia. Additionally, the macroscale developmental pattern is also associated with myelin content and thicknesses of specific laminas. These findings offer insights into the underlying genetics and neural mechanisms of macroscale white matter connectome development from childhood to adolescence.


Statistics
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The exact sample size (n) for each experimental group/condition, given as a discrete number and unit of measurement A statement on whether measurements were taken from distinct samples or whether the same sample was measured repeatedly The statistical test(s) used AND whether they are one-or two-sided Only common tests should be described solely by name; describe more complex techniques in the Methods section.

A description of all covariates tested
A description of any assumptions or corrections, such as tests of normality and adjustment for multiple comparisons A full description of the statistical parameters including central tendency (e.g.means) or other basic estimates (e.g.regression coefficient) AND variation (e.g. standard deviation) or associated estimates of uncertainty (e.g.confidence intervals) For null hypothesis testing, the test statistic (e.g.F, t, r) with confidence intervals, effect sizes, degrees of freedom and P value noted Give P values as exact values whenever suitable.
For Bayesian analysis, information on the choice of priors and Markov chain Monte Carlo settings For hierarchical and complex designs, identification of the appropriate level for tests and full reporting of outcomes Estimates of effect sizes (e.g.Cohen's d, Pearson's r), indicating how they were calculated Our web collection on statistics for biologists contains articles on many of the points above.

Software and code
Policy information about availability of computer code Data collection No software was used in the data collection process.
For manuscripts utilizing custom algorithms or software that are central to the research but not yet described in published literature, software must be made available to editors and reviewers.We strongly encourage code deposition in a community repository (e.g.GitHub).See the Nature Portfolio guidelines for submitting code & software for further information.

April 2023
Reporting for specific materials, systems and methods We require information from authors about some types of materials, experimental systems and methods used in many studies.Here, indicate whether each material, system or method listed is relevant to your study.If you are not sure if a list item applies to your research, read the appropriate section before selecting a response.

Novel plant genotypes
Describe the methods by which all novel plant genotypes were produced.This includes those generated by transgenic approaches, gene editing, chemical/radiation-based mutagenesis and hybridization.For transgenic lines, describe the transformation method, the number of independent lines analyzed and the generation upon which experiments were performed.For gene-edited lines, describe the editor used, the endogenous sequence targeted for editing, the targeting guide RNA sequence (if applicable) and how the editor was applied.

Seed stocks
Report on the source of all seed stocks or other plant material used.If applicable, state the seed stock centre and catalogue number.If plant specimens were collected from the field, describe the collection location, date and sampling procedures.

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Describe any authentication procedures for each seed stock used or novel genotype generated.Describe any experiments used to assess the effect of a mutation and, where applicable, how potential secondary effects (e.g.second site T-DNA insertions, mosiacism, off-target gene editing) were examined.

Plants Magnetic resonance imaging Experimental design Design type
Resting state.

Design specifications
We train participants to remain still using mock scanning, offer prizes and praise, keep the participants busy while in the scanner with movies (for the structural and diffusion scans), constrain the head in space with pillows and tape, and conduct the MRI scanning toward the beginning of study visits whenever possible.
Behavioral performance measures No behavioral performance measures were used in this study.Noise and artifact removal

Acquisition
The eddy current distortions and motion artefacts in the dMRI data were corrected by applying an affine alignment of each DWI image to the b0 image.

Volume censoring
Each T1 image was ensured the absence of arachnoid cysts, neuroepithelial cysts, or any other intracranial occupying lesions.Subsequently, five trained raters visual inspected the T1 images for brain damage, missing layers, or evident noise.For dMRI image, images reported as failures by DTIprep were excluded.Additionally, visual inspections by five trained raters were conducted, and images with abnormal volume proportions exceeding 10% were excluded.

Statistical modeling & inference
Model type and settings The BNA246 template was used to define brain locations.Briefly, a b0 image was first aligned to a native T1 image, and then the native T1 image was normalized to an asymmetric T1 template for 6-12 years from Chinese Paediatric Atlases using the FMRIB Software Library (https://fsl.fmrib.ox.ac.uk/fsl).Inverse transformation matrices derived from the aforementioned steps were applied to transform the brain atlas of standard space into native space.
Statistic type for inference

Graph analysis
Based on the tractography results, the FA×FN-weighted network of each participant was constructed, where the FA×FN weight was defined as the average FA value of the voxels traversed along the connected fibres between two regions times the number of fibre streamlines (FN) connecting two brain regions.Eight wholebrain properties were calculated according to the constructed network, including global efficiency, local efficiency, shortest path, network strength, clustering coefficient, and small-world parameters (γ, λ and σ).
For each brain region, four common nodal properties were calculated: nodal efficiency, nodal local efficiency, nodal degree centrality and nodal betweenness centrality.According to the different categories of two nodes, the existing edges between them were classified into three types: local (nonhub to nonhub), feeder (hub to nonhub) and rich-club (hub to hub).
Multivariate modeling and predictive analysis Partial least square correlation was performed to mine the weighted linear combinations of gene expression profiles associated with the spatial development slopes of the WM nodal efficiency.
was first aligned to a native T1 image, and then the native T1 image was normalized to an asymmetric T1 template for 6-12 years from Chinese Paediatric Atlases using linear and nonlinear registration from the FMRIB Software Library (https://fsl.fmrib.ox.ac.uk/fsl).The 6-12 years from Chinese Paediatric Atlases.Zhao, T., et al.Unbiased age-specific structural brain atlases for Chinese pediatric population.Neuroimage 189, 55-70 (2019) FMRIB's Diffusion Toolbox of the FMRIB Software Library v6.0.eddy_correct dti.nii.gzeddyDti.nii.gz0 nature portfolio | reporting summary Both linear and quadratic models were estimated by a mixed effect model to characterize the intrinsic longitudinal relationship between brain network properties and age.Total brain volume, centre and sex were considered as covariate.