Fig 1.
Manual definition of an MFC ROI.
(a) Probabilistic MFC ROI. The scale bar denotes the number of cases mapped onto a voxel which ranged from 0 to 12. (b) Probabilistic MFC ROI threshold at 75%. (c) ROI mapped onto the cortical surface.
Fig 2.
Overview of procedures to parcellate the MFC using cortical thickness and fMRI.
TOP: Procedures for pre-processing fMRI and T1-weighted MRI. MIDDLE: Procedures for constructing the similarity matrix. BOTTOM: Procedures for parcellating the MFC regions into k sub-regions using spectral and Ward’s clustering approaches.
Fig 3.
Stability of connectivity patterns.
(a) Map of correlation coefficients describing the average correlation between MFC cortical thickness-driven connectivity patterns (between activity in the MFC and the rest of the brain). (b) Histogram of correlation coefficients between the MFC connectivity patterns of different subjects based on thickness. (c) Map of correlation coefficients describing the average correlation between MFC fMRI-driven connectivity patterns (between activity in the MFC and the rest of the brain). (d) Histogram of correlation coefficients between the MFC connectivity patterns of different subjects based on fMRI.
Fig 4.
Choosing the number of clusters.
(a) Plot of VI values with respect to the number of clusters (k = 2..10) for two clustering algorithms (spectral and Ward’s clustering approaches). All VI values at different k values were statistically significant (p < 0.05). There was no plateau as we decreased k from 10 to 2. (b) Plot of silhouette coefficient with respect to the number of clusters (k = 2..10) for the spectral clustering approach. (c) Plot of silhouette coefficient with respect to the number of clusters (k = 2…10) for Ward’s clustering approach.
Fig 5.
Parcellation results using (a) connectivity from fMRI and (b) connectivity from cortical thickness based on Ward’s clustering with K = 2. Parcellation results using (c) connectivity from fMRI and (d) connectivity from cortical thickness based on spectral clustering with K = 2. The anterior portion is shown in green for the pre-SMA and the posterior portion is shown in red for the SMA.
Fig 6.
Overlap between BA6 and parcellation results using two clustering approaches based on two types of connectivity.
(a) Using fMRI and Ward’s clustering. (b) Using fMRI and spectral clustering. (c) Using thickness and Ward’s clustering. (d) Using thickness and spectral clustering.
Fig 7.
fMRI-driven connectivity results of the whole brain using the MFC sub-region as the seed region.
(a) Connectivity results using the left pre-SMA (i.e., anterior) region as the seed region. (b) Connectivity results using the right pre-SMA region as the seed region. (c) Connectivity results using the left SMA (i.e., posterior) region as the seed region. (d) Connectivity results using the right SMA region as the seed region. Colored values denote negative log of p-values.
Table 1.
Major correlated regions in the motor and frontal areas for SMA/pre-SMA regions using fMRI data.
Regions with significance are reported in bold.
Fig 8.
Cortical thickness-driven network analysis of the whole brain using MFC sub-regions as the seed region.
(a) Connectivity results using the left pre-SMA (i.e., anterior) region as the seed region. (b) Connectivity results using the right pre-SMA region as the seed region. (c) Connectivity results using the left SMA (i.e., posterior) region as the seed region. (d) Connectivity results using the right SMA region as the seed region. Colored values denote z-values.
Table 2.
Major correlated regions (motor and frontal regions) for SMA/pre-SMA regions using cortical thickness.
Regions with significance are reported in bold.