Research ArticleCOGNITIVE NEUROSCIENCE

Inversion of a large-scale circuit model reveals a cortical hierarchy in the dynamic resting human brain

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Science Advances  09 Jan 2019:
Vol. 5, no. 1, eaat7854
DOI: 10.1126/sciadv.aat7854
  • Fig. 1 Automatic optimization of rMFM parameters yields stronger agreement between empirical and simulated RSFC.

    (A) 68 × 68 empirical FC matrix of 68 ROIs from HCP test set (n = 226). (B) 68 × 68 SC matrix from the HCP test set. (C) Simulated 68 × 68 FC matrix using SC matrix from the test set and rMFM parameters estimated from the HCP training set (n = 226). (D) Correlation between inter-region simulated FC and inter-region empirical FC (ignoring diagonal elements of the matrices). Correlation between SC and empirical FC in the test set was 0.30. Correlation between simulated and empirical FC was 0.46.

  • Fig. 2 Strength of recurrent connections w and subcortical inputs I in 68 anatomically defined ROIs and their relationships with seven resting-state networks.

    (A) Strength of recurrent connection w in 68 anatomically defined ROIs. (B) Strength of excitatory subcortical input I in 68 anatomically defined ROIs. Parcels correspond to the 68 Desikan-Killiany ROIs (26). Black boundaries correspond to the boundaries of seven canonical resting-state networks (19). (C) Seven resting-state networks (19). (D) Strength of recurrent connections w in the seven resting-state networks. (E) Strength of subcortical input I in the seven resting-state networks. Regions within sensory-motor systems exhibited strong recurrent connections and excitatory subcortical input, while those within the default network exhibited weak recurrent connections and excitatory subcortical input.

  • Fig. 3 Relationship between recurrent connection strength w and BrainMap cognitive components.

    (A) 68 Desikan-Killiany ROIs are grouped into 10 zones spanning low to high recurrent connection strength w. (B) Twelve cognitive components derived from meta-analysis of 10,449 experiments (20) are ordered on the basis of the average normalized activation strength within each of the 10 zones. Zones with high recurrent connection strength were involved in sensory perception and motor actions (visual, auditory, hand, and face), while those with low recurrent connection strength were involved in cognitive functions, such as working memory, internal mentation, and reward.

  • Fig. 4 Associations of estimated rMFM parameters (strength of recurrent connection w and subcortical input I) with the first principal RSFC gradient and relative myelin content.

    (A) First principal RSFC gradient obtained by diffusion embedding of the human connectome (6). (B) Association between recurrent connection w and first principal gradient. (C) Association between subcortical input I and first principal gradient. (D) T1w/T2w ratio map of estimated myelin content (21). (E) Association between recurrent connection w and myelin. (F) Association between subcortical input I and myelin.

  • Fig. 5 Associations between estimated rMFM parameters (strength of recurrent connection w and subcortical input I) and cytoarchitectonic measures (neuronal density and neuronal size) averaged across all cortical layers.

    (A) Association between recurrent connection w and neuronal density averaged across all cortical layers. (B) Association between recurrent connection w and neuronal size averaged across all cortical layers. (C) Association between subcortical input I and neuronal density averaged across all cortical layers. (D) Association between subcortical input I and neuronal size averaged across all cortical layers.

  • Table 1 Pearson’s correlation between estimated rMFM parameters (recurrent connection w and subcortical input I) and cytoarchitectonic data (neuronal cell density and cell size).

    P values that survived a false discovery rate of q < 0.05 are bolded.

    wPIP
    Layer 1 density−0.130.480.340.053
    Layer 2 density0.500.00380.230.21
    Layer 3 density0.520.00150.0780.66
    Layer 4 density0.390.036−0.100.60
    Layer 5 density−0.110.540.240.17
    Layer 6 density0.560.000540.230.20
    Cell density averaged across all layers0.550.000710.0500.78
    Layer 1 size−0.290.0900.0260.88
    Layer 2 size−0.260.15−0.170.37
    Layer 3 size0.200.25−0.0420.81
    Layer 4 size0.310.10−0.0520.79
    Layer 5 size0.260.140.130.46
    Layer 6 size−0.180.30−0.180.31
    Cell size averaged across all layers0.190.280.0310.86

Supplementary Materials

  • Supplementary material for this article is available at http://advances.sciencemag.org/cgi/content/full/5/1/eaat7854/DC1

    Fig. S1. Relationship between subcortical input I and BrainMap cognitive components.

    Fig. S2. Strength of recurrent connections w and subcortical input I in 114 anatomically defined ROIs and their relationships with seven resting-state networks.

    Fig. S3. Relationship between recurrent connection strength w and BrainMap cognitive components.

    Fig. S4. Relationship between subcortical input I and BrainMap cognitive components.

    Fig. S5. Associations of estimated rMFM parameters (using the Lausanne 2008 parcellation) with relative myelin content and first principal gradient of the human connectome.

    Fig. S6. Relationships between cortical types and estimated rMFM parameters.

    Table S1. Top 5 tasks recruiting 12 cognitive components (20).

    Table S2. Pearson’s correlation between estimated rMFM parameters (recurrent connection w and subcortical input I) using the Lausanne 2008 parcellation and cytoarchitectonic data (neuronal cell density and cell size).

  • Supplementary Materials

    This PDF file includes:

    • Fig. S1. Relationship between subcortical input I and BrainMap cognitive components.
    • Fig. S2. Strength of recurrent connections w and subcortical input I in 114 anatomically defined ROIs and their relationships with seven resting-state networks.
    • Fig. S3. Relationship between recurrent connection strength w and BrainMap cognitive components.
    • Fig. S4. Relationship between subcortical input I and BrainMap cognitive components.
    • Fig. S5. Associations of estimated rMFM parameters (using the Lausanne 2008 parcellation) with relative myelin content and first principal gradient of the human connectome.
    • Fig. S6. Relationships between cortical types and estimated rMFM parameters.
    • Table S1. Top 5 tasks recruiting 12 cognitive components (20).
    • Table S2. Pearson’s correlation between estimated rMFM parameters (recurrent connection w and subcortical input I) using the Lausanne 2008 parcellation and cytoarchitectonic data (neuronal cell density and cell size).

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