Characterization of dFC states

Regarding the frontoparietal network, Sadaghiani and colleagues found that alpha synchronization, i.e, increased alpha power, was positively correlated to cognitive functions associated with the fron- toparietal network [89]. Furthermore, a recent study found that this network had a diminished expression after the administration of psilocybin [48]. EEG studies with psilocybin have shown decreased parieto- occipital alpha power [90]. Thus, the association of high alpha power with this network is consistent with these findings.

With respect to state 3, the functionally connected regions that oppose the global mode are regions that are also found connected in theDMN. It may suggest that sub-parts of this network are functionally connected at certain times and at other instants they may connected with other regions. This would, at a certain extent, explain the association of such network with high alpha power. However, further studies need to be performed to understand why high alpha power, mainly parieto-occipital alpha, is associated with these states.

Figure 3.13:States (centroids) 3, 5 and 6 obtained by clustering the leading vectors of BOLD phase coherence with K = 9. On the top is represented the network in cortical space, where the values of the centroid vector are used to scale the color of each brain area and the connections (blue links) are plotted between brain areas with a value<−0.1, highlighting the network contrasting from the global mode. On the middle is represented the dFC matrix obtained by computing the outer product of centroid vector. On the bottom are represented the elements of each centroid vector and the corresponding brain regions.

In blue (negative values) are the brain regions that belong to the network represented on top left.

Figure 3.14:dFC states obtained with K-means clustering for K=9. For each state (cluster) is represented the centroid vector in cortical space, below is the matrix obtained by computing the outer product of the centroid vector and further below the bar plot of the centroid vector.

parahippocampus, thereby not being correlated to anyRSNin specific.

Regarding the remaining states, the significant correlations (p-value<0.01) are the following:

• State 4 shows a significant correlation with the visual network (r =0.80, p-value =5.1∗10⁻²¹);

• State 5 significantly correlates with theDMN(r =0.46, p-value =5.9∗10⁻⁶);

• State 6 has a significant correlation with the frontoparietal network (r =0.42, p-value =3.7∗10⁻⁵);

• State 7 significantly correlates with the ventral attention network (r =0.39, p-value =1.8∗10⁻⁴), also registering a smaller correlation with the frontoparietal network (r =0.23) which has a p-value

<0.05;

• State 8 shows a significant correlation with the somatomotor network (r = 0.63, p-value = 2.1∗ 10⁻¹¹) and also has smaller correlation with the ventral attention network (r =0.23) with p-value

<0.05;

Figure 3.15:Seven resting-state networks defined inYeo et al. (2011)[85]. On top are represented the cortical networks after transforming into AAL space by counting the number of 2mm³ MNI voxels in each AAL brain area belonging to each of the 7 networks, resulting in a vector with 90 elements. As the negative values of the centroid vectors are the ones that represent the network contrasting from the global mode, the RSN vectors were transformed to its symmetric so that they could be compared with the centroid vectors. On the bottom is the representation of each RSN vector in cortical space.

Figure 3.16:Pearson Correlation between all centroid vectors obtained with K-means clustering for K=9 and the 7 RSNs defined by Yeo and colleagues [85]. The asterisks indicate significant correlations with p-value

<0.01.

Although some states were significantly correlated with theseRSNs, it does not mean that they are not functionally connected to other brain regions. In fact, in thedFCstates found, for example theDMN have connections with the anterior cingulate cortex (associated to the salience network), and the fron- toparietal is linked to the angular gyrus (usually associated to theDMN). RSNs are found through static functional connectivity analyses, i.e, when the wholeBOLDsignal time-course of different brain regions is analyzed at once, whiledFC states, as the name implies, represent recurrent connectivity patterns that appear over time. So, it seems plausible that thedFCstates show connectivity between regions of known resting-state networks, but revealing also connectivity with other regions, which in the static may not be found. As Catie Chang and colleagues reported [3], the posterior cingulate cortex, which is a region from theDMN, i.e, theBOLDsignal in this region is positively correlated to theBOLDsignal from the otherDMNregions, was found to have different correlations with brain regions not belonging to the DMNat different time points.

Regarding the dynamic parameters of thedFCstates namely the fractional occupancy, computed as the number ofTRs each state was active over the total number ofTRs, the mean lifetime, computed as the mean number ofTRs each state was active, and the transition probabilities are shown in Figure 3.17.

Figure 3.17:Dynamic parameters of the 9 dFC states. On the top left corner is represented the fractional occu- pancy, computed as the number of TRs each state was active over the total number of TRs. On the top right corner is represented the mean lifetime across all TRs and the error bars indicate the standard error. On the bottom is the transition matrix; the values shown in the matrix are probabilities higher than 0.15.

State 1, which is the state where there is a global coherence of BOLD phases, has the highest fractional occupancy (0.32) and the longest mean lifetime (17.8±1.8 TRs). Furthermore, all states except one had the highest transition probability to state 1.

These results indicate that the BOLDphases after spending some time misaligned with the global signal (and aligned between them), most of the time they realign with the global signal, returning to a state where all phases have the same direction.

Regarding the mean lifetime, it is important to note that all states have a mean duration greater than 10 TRs due to the temporal smoothing applied to the labels after the K-means clustering. Although it was not a strict smoothing, but a balanced smoothing which took into account the distance of each leading eigenvector to its centroid, as well as the neighbouring labels, the minimum mean lifetime could have been slightly below 10TR, however with this number of states they were all above 10TR.