sunlianglong / BrainChart-FC-Lifespan

Functional connectome through the human life span

First revision date: 15/09/2023

Second revision date: 30/05/2024

E-mail: [e-mail]

This repository provides code and source data that support the findings of the article entitled "Functional connectome through the human life span" by Sun et al. (2023) bioRxiv. https://www.biorxiv.org/content/10.1101/2023.09.12.557193v2

Data

• Growth_curve_global_mean_of_FC.mat
  • The lifespan normative growth curve of the global mean of functional connectome
• Growth_curve_global_variance_of_FC.mat
  • The lifespan normative growth curve of the global variance of functional connectome
• Growth_curve_global_atlas_similarity.mat
  • The lifespan normative growth curve of the global atlas similarity (individual level)
• Growth_curve_global_system_segregation.mat
  • The lifespan normative growth curve of the global system segregation
• Growth_curve_VIS_system_segregation.mat
  • The lifespan normative growth curve of the system segregation of VIS network
• Growth_curve_SM_system_segregation.mat
  • The lifespan normative growth curve of the system segregation of SM network
• Growth_curve_DA_system_segregation.mat
  • The lifespan normative growth curve of the system segregation of DA network
• Growth_curve_VA_system_segregation.mat
  • The lifespan normative growth curve of the system segregation of VA network
• Growth_curve_LIM_system_segregation.mat
  • The lifespan normative growth curve of the system segregation of LIM network
• Growth_curve_FP_system_segregation.mat
  • The lifespan normative growth curve of the system segregation of FP network
• Growth_curve_DM_system_segregation.mat
  • The lifespan normative growth curve of the system segregation of DM network  
• Lifespan_growth_axis.fs4.L(R).func.gii
  • The lifespan growth axis (in fsaverage4 space) of brain functional connectivity, represented by the first principal component from a PCA on vertex-level FCS curves.
• Lifespan_growth_axis.fsLR32k.L(R).func.gii
  • The lifespan growth axis in fs_LR_32k space, ressampled from Lifespan_growth_axis.fs4.L(R).func.gii
• S-A_axis.fsLR32k.L(R).func.gii
  • The sensorimotor-association axis (in fs_LR_32k space), as formulated by Sydnor et al. [ Sydnor, V.J., et al. Neurodevelopment of the association cortices: Patterns, mechanisms, and implications for psychopathology. Neuron (2021) ]
• S-A_axis.fs4.L(R).func.gii
  • The sensorimotor-association axis in fsaverage4 space, ressampled from S-A_axis.fsLR32k.L(R).func.gii

 

Age-specific Group Atlas

Atlas	Age Range	Atlas file in fsaverage4 space	Atlas file in fs_LR_32k space
34-week atlas	32-35 postmenstrual weeks	Atlas1_fs4_L(R).label.gii	Atlas1_fsLR32k_L(R).label.gii
36-week atlas	35-37 postmenstrual weeks	Atlas2_fs4_L(R).label.gii	Atlas2_fsLR32k_L(R).label.gii
38-week atlas	37-39 postmenstrual weeks	Atlas3_fs4_L(R).label.gii	Atlas3_fsLR32k_L(R).label.gii
40-week (0-month) atlas	39-41 postmenstrual weeks	Atlas4_fs4_L(R).label.gii	Atlas4_fsLR32k_L(R).label.gii
1-month atlas	0.25-1.5 months	Atlas5_fs4_L(R).label.gii	Atlas5_fsLR32k_L(R).label.gii
3-month atlas	1.5-4.5 months	Atlas6_fs4_L(R).label.gii	Atlas6_fsLR32k_L(R).label.gii
6-month atlas	4.5-7.5 months	Atlas7_fs4_L(R).label.gii	Atlas7_fsLR32k_L(R).label.gii
9-month atlas	7.5-10.5 months	Atlas8_fs4_L(R).label.gii	Atlas8_fsLR32k_L(R).label.gii
12-month atlas	10.5-13.5 months	Atlas9_fs4_L(R).label.gii	Atlas9_fsLR32k_L(R).label.gii
18-month atlas	13.5-21 months	Atlas10_fs4_L(R).label.gii	Atlas10_fsLR32k_L(R).label.gii
24-month atlas	21-27 months	Atlas11_fs4_L(R).label.gii	Atlas11_fsLR32k_L(R).label.gii
4-year atlas	2.25-5 years	Atlas12_fs4_L(R).label.gii	Atlas12_fsLR32k_L(R).label.gii
6-year atlas	5-7 years	Atlas13_fs4_L(R).label.gii	Atlas13_fsLR32k_L(R).label.gii
8-year atlas	7-9 years	Atlas14_fs4_L(R).label.gii	Atlas14_fsLR32k_L(R).label.gii
10-year atlas	9-11 years	Atlas15_fs4_L(R).label.gii	Atlas15_fsLR32k_L(R).label.gii
12-year atlas	11-13 years	Atlas16_fs4_L(R).label.gii	Atlas16_fsLR32k_L(R).label.gii
14-year atlas	13-15 years	Atlas17_fs4_L(R).label.gii	Atlas17_fsLR32k_L(R).label.gii
16-year atlas	15-17 years	Atlas18_fs4_L(R).label.gii	Atlas18_fsLR32k_L(R).label.gii
18-year atlas	17-19 years	Atlas19_fs4_L(R).label.gii	Atlas19_fsLR32k_L(R).label.gii
20-year atlas	19-21 years	Atlas20_fs4_L(R).label.gii	Atlas20_fsLR32k_L(R).label.gii
30-year atlas	25-35 years	Atlas21_fs4_L(R).label.gii	Atlas21_fsLR32k_L(R).label.gii
40-year atlas	35-45 years	Atlas22_fs4_L(R).label.gii	Atlas22_fsLR32k_L(R).label.gii
50-year atlas	45-55 years	Atlas23_fs4_L(R).label.gii	Atlas23_fsLR32k_L(R).label.gii
60-year atlas	55-65 years	Atlas24_fs4_L(R).label.gii	Atlas24_fsLR32k_L(R).label.gii
70-year atlas	65-75 years	Atlas25_fs4_L(R).label.gii	Atlas25_fsLR32k_L(R).label.gii
80-year atlas	75-80 years	Atlas26_fs4_L(R).label.gii	Atlas26_fsLR32k_L(R).label.gii

 

Code

Quality control for raw images

• MRIQC v0.15.0 (https://github.com/nipreps/mriqc)

Data preprocessing

• QuNex v0.93.2 (https://gitlab.qunex.yale.edu/)
• HCP pipeline v4.4.0-rc-MOD-e7a6af9 (https://github.com/Washington-University/HCPpipelines/releases)
• ABCD-HCP pipeline v1 (https://github.com/DCAN-Labs/abcd-hcp-pipeline)
• dHCP structural pipeline v1 (https://github.com/BioMedIA/dhcp-structural-pipeline)
• dHCP functional pipeline v1 (https://git.fmrib.ox.ac.uk/seanf/dhcp-neonatal-fmri-pipeline)
• iBEAT pipeline v1.0.0 (https://github.com/iBEAT-V2/iBEAT-V2.0-Docker)

Data postprocessing

• MATLAB R2018b (https://www.mathworks.com/products/matlab.html)
• SPM12 toolbox v6470 (https://www.fil.ion.ucl.ac.uk/spm/software/spm12)
• GRETNA toolbox v2.0.0 (https://www.nitrc.org/projects/gretna)
• cifti-matlab toolbox v2 (https://github.com/Washington-University/cifti-matlab)

for-Population-Level-Atlases

• Atlas_Construction
  • Preparation Information for subjects: data paths, age information, age-specific individual variability map, and age-specific tSNR map.
• Adult-based group atlas
• Atlas_Generation.m
  • To establish a series of age-specific group-level atlases with accurate system correspondences over the lifespan, we proposed a Gaussian-weighted iterative age-specific group atlas (GIAGA) generation approach. For detailed information, please refer to https://www.biorxiv.org/content/10.1101/2023.09.12.557193v2
  • Before running this code, you need to prepare at least one set of individual BOLD data in the fsaverage4 space.

for-Individualized-Atlases

• fun_Run_IndiPara_for_Each_Indi.m
  • Using the individualized parcellation method proposed by Wang et al. [ Wang, D., et al. Parcellating cortical functional networks in individuals. Nature Neuroscience 18, 1853-1860 (2015) ], we generate person-specific parcellation atlas for each individual.
  • Before running this code, you need to prepare at least one set of individual BOLD data in the fsaverage4 space.

for-Normative-Modeling

• GAMLSS_model_fitting.ipynb
  • R function for GAMLSS model specification and estimation
  • R v4.2.0 (https://www.r-project.org)
  • GAMLSS package v5.4-3 (https://www.gamlss.com/)
• data.csv
  • The data to be loaded when running GAMLSS_model_fitting.ipynb
• Model_global system segregation.RData
  • Precomputed model

for-Visualization

• plot_1_Normative_Growth_Curve.m
  • Plot the normative growth curve
• plot_2_Normative_Growth_Rate.m
  • Plot the growth rate
• plot_3_Normative_Growth_Curve_female_male.m
  • Plot the normative growth curve for famale and male
• MATLAB R2018b (https://www.mathworks.com/products/matlab.html)
• BrainNet Viewer toolbox v 20191031 (https://www.nitrc.org/projects/bnv)
• Connectome Workbench v1.5.0 (https://www.humanconnectome.org/software/connectome-workbench)
• R v4.2.0 (https://www.r-project.org)
• ggplot2 package v3.4.2 (https://ggplot2.tidyverse.org/)

Functions_Masks_Templates

• Essential libraries required for running the code