First revision date: 15/09/2023
Second revision date: 30/05/2024
E-mail: lianglongsun@mail.bnu.edu.cn
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
- 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
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 |
- MRIQC v0.15.0 (https://github.com/nipreps/mriqc)
- 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)
- 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)
- 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.
- 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.
- 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
- 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/)
- Essential libraries required for running the code