Spatial and temporal pattern of structure–function coupling of human brain connectome with development
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, China
- BABRI Centre, Beijing Normal University, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, China
- School of Computer Science and Engineering, Beihang University, China
Figures
Figure 1 with 1 supplement
Structural connectome–functional connectome (SC–FC) coupling framework.
The framework used to quantify nodal SC–FC coupling in the human brain. The microstructure profile covariance (MPC) was used to map similarity networks of intracortical microstructure (voxel …
Figure 1—figure supplement 1
Pipeline of communication model selection and reproducibility analyses.
Figure 2 with 1 supplement
Cortical structural connectome–functional connectome (SC–FC) coupling in young individuals.
(A) Spatial pattern of SC–FC coupling. (B) Spatial patterns with significant predictions (p < 0.05, spin test). (C) SC–FC coupling comparisons among functional networks. The error bars represent 95% …
Figure 2—figure supplement 1
Comparison results between different models.
Spatial pattern of mean structural connectome–functional connectome (SC–FC) coupling based on microstructure profile covariance (MPC) ~ FC (A), SCs ~ FC (B), and MPC + SCs ~ FC (C). Correlation of …
Figure 3 with 4 supplements
Age-related changes in structural connectome–functional connectome (SC–FC) coupling.
(A) Increases in whole-brain coupling with age. (B) Correlation of age with SC–FC coupling across all regions and significant regions (p < 0.05, FDR corrected). (C) Comparisons of age-related …
Figure 3—figure supplement 1
Age-related changes in microstructure profile covariance (MPC) weight.
(A) Increases in MPC weight across the whole brain with age. (B) Correlation of age with MPC weight across significant regions (p < 0.05, FDR corrected). (C) Comparison of changes in MPC weight …
Figure 3—figure supplement 2
Age-related changes in communicability weight.
(A) Increases in communicability weight across the whole brain with age. (B) Correlation of age with communicability weight across significant regions (p < 0.05, FDR corrected). (C) Comparison of …
Figure 3—figure supplement 3
Age-related changes in flow graph weight.
(A) Increases in flow graph weight across the whole brain with age. (B) Correlation of age with flow graph weight across significant regions (p < 0.05, FDR corrected). (C) Flow graphs of changes in …
Figure 3—figure supplement 4
Age-related changes in the weight of the mean first-passage time.
(A) Increases in the weight of the mean first-passage time across the whole brain with age. (B) Correlation of age with mean first-passage time weight across significant regions (p < 0.05, FDR …
Figure 4 with 1 supplement
Encoding individual differences in intelligence using regional structural connectome–functional connectome (SC–FC) coupling.
(A) Predictive accuracy of fluid, crystallized, and general intelligence composite scores. (B) Regional distribution of predictive weight. (C) Predictive contribution of functional networks. Each …
Figure 4—figure supplement 1
Predictive accuracy of regional structural connectome–functional connectome (SC–FC) coupling across cognitive measures.
The top panel shows the predictive accuracy of regional SC–FC coupling across cognitive measures not adjusted for age, and the bottom panel shows the predictive accuracy across age-adjusted …
Figure 5 with 1 supplement
Association between developmental changes in structural connectome–functional connectome (SC–FC) coupling and gene transcriptional profiles.
(A) The map of developmental changes (absolute value of correlation coefficients) in SC–FC coupling across 105 left brain regions (left panel), and the normalized gene transcriptional profiles …
Figure 5—figure supplement 1
Cell-specific expression in each pathway.
(A) Genes with positive weights. (B) Genes with negative weights. Note: pperm: permutation test.
Figure 6
Reproducibility analyses with different parcellation templates (HCPMMP).
(A) Spatial pattern of structural connectome–functional connectome (SC–FC) coupling. (B) Correlation of age with SC–FC coupling. (C) Correlation of age with SC–FC coupling across significant regions …
Figure 7
Reproducibility analyses with different tractography strategies.
(A) The consistency of mean structural connectome–functional connectome (SC–FC) coupling between deterministic and probabilistic tractography. (B) The consistency of the correlation between age and …
Figure 8
Reproducibility analyses with split-half validation.
(A) The consistency of mean structural connectome–functional connectome (SC–FC) coupling among S1, S2, and the whole dataset (WD). (B) The consistency of the correlation between age and SC–FC …
Figure 9 with 2 supplements
Exclusion of participants in the whole multimodal data processing pipeline.
Figure 9—figure supplement 1
Correlations between motion and age and number of remaining frames and age.
Figure 9—figure supplement 2
Cognitive scores and age distributions of scans.
Author response image 1
The consistency of gene weights between HCPMMP and BNA.
Author response image 2
Cognitive scores and age distributions of scans.
Author response image 3
Aged-related changes in SC-FC coupling.
(A) Increases in whole-brain coupling with age. (B) Correlation of age with SC-FC coupling across all regions and significant regions (p<0.05, FDR corrected). (C) Comparisons of age-related changes …
Author response image 4
Pipeline of model selection and reproducibility analyses.
Author response image 5
Comparison results between different models.
Spatial pattern of mean SC-FC coupling based on MPC ~ FC (A), SCs ~ FC (B), and MPC + SCs ~ FC (C). Correlation of age with SC-FC coupling across cortex based on MPC ~ FC (D), SCs ~ FC (E), and MPC …
Author response image 6
Encoding individual differences in intelligence using regional SC-FC coupling.
(A) Predictive accuracy of fluid, crystallized, and general intelligence composite scores. (B) Regional distribution of predictive weight. (C) Predictive contribution of functional networks. The …
Author response image 7
Exclusion of participants in the whole multimodal data processing pipeline.
Author response image 8
Figure S14.
Correlations between motion and age and number of remaining frames and age.
Tables
Table 1
Predictive significance of the communication model.
| Predictor | pspin | Predictor | pspin | ||
|---|---|---|---|---|---|
| Shortest path length | Gamma values = 0.12 | 0.93 | Path transitivity | Weight-to-cost transformations = 0.12 | 0.84 |
| Gamma values = 0.25 | 0.69 | Weight-to-cost transformations = 0.25 | 0.97 | ||
| Gamma values = 0.5 | 0.63 | Weight-to-cost transformations = 0.5 | 0.90 | ||
| Gamma values = 1 | 0.89 | Weight-to-cost transformations = 1 | 0.75 | ||
| Gamma values = 2 | 0.77 | Weight-to-cost transformations = 2 | 0.90 | ||
| Gamma values = 4 | 0.45 | Weight-to-cost transformations = 4 | 0.61 | ||
| Communicability | <0.001 | Matching index | 0.42 | ||
| Cosine similarity | 0.25 | Greedy navigation | 0.99 | ||
| Search information | Weight-to-cost transformations = 0.12 | 0.63 | Mean first-passage times of random walkers | 0.01 | |
| Weight-to-cost transformations = 0.25 | 0.59 | Flow graphs | Timescales = 1 | <0.001 | |
| Weight-to-cost transformations = 0.5 | 0.32 | Timescales = 2.5 | 0.26 | ||
| Weight-to-cost transformations = 1 | 0.72 | Timescales = 5 | 0.91 | ||
| Weight-to-cost transformations = 2 | 0.60 | Timescales = 10 | 0.80 | ||
| Weight-to-cost transformations = 4 | 0.75 | ||||
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Note: pspin: spin test. The communication models in bold provide the optimal combination.
Author response table 1
| In-scanner head motion | Intracranial volume | |||
|---|---|---|---|---|
| r value | p value | r value | p value | |
| Episodic memory | -0.04 | 0.494825 | 0.00 | 0.963828 |
| Executive function/cognitive flexibility | -0.13 | 0.017108 | -0.02 | 0.729339 |
| Executive function/inhibition, language/reading decoding | 0.00 | 0.985376 | -0.01 | 0.911467 |
| Language/reading decoding | -0.09 | 0.124475 | 0.03 | 0.596947 |
| Processing speed | -0.07 | 0.215465 | 0.11 | 0.049091 |
| Language/vocabulary comprehension | -0.15 | 0.007839 | 0.01 | 0.835508 |
| Working memory | -0.02 | 0.767743 | 0.08 | 0.16235 |
| Fluid intelligence composite score Crystal intelligence composite score | -0.12 | 0.034428 | 0.02 | 0.695776 |
| Early child intelligence composite | -0.09 | 0.112609 | 0.08 | 0.153797 |
| score | -0.09 | 0.107082 | 0.04 | 0.503285 |
| Total intelligence composite score | -0.12 | 0.037287 | 0.07 | 0.219497 |
| Age-adjusted cognitive score |
