Table 1.
Demographic variables of MCI patients and healthy controls at initial screening. After a 2-year follow up, 6 MCI subjects had converted to AD.
Fig 1.
Trials were conducted as follows. A warning screen for 2500ms, a fixation period of 3000ms where a white cross turns from white to black, a blank grey screen for 250ms, a reminder of the instructions for 2000ms, the shapes or shapes with colours (depending on shape or binding task) are displayed for 2000ms (encoding phase), a blank grey screen is displayed for a variable time of (2000, 4000, 6000, or 8000ms) due to the fMRI design optimisation [27] (maintenance phase), a second set of shapes or shapes with colours are displayed for 4000ms (probe phase), and lastly an inter-trial interval of 2000ms. Repeat.
Fig 2.
Multiplex network of the VSTMBT and nQ example.
a) Networks for the two task phases of the VSTMBT, encmaint and probe, are constructed from the functional co-activations (correlation in time-series) between all pairs of ROIs. Spatial replicas are connected via an inter-layer edge, as seen in the above figure (light grey edges between the two layers), allowing for continuity of network topology in time. b) Here, multiplex nodal modularity (nQ) has been calculated for each node in our network as in Eq 1 (refer to the following section). Each colour represents a separate module (obtained from standard multiplex modularity [25], where modules can exist within a layer (blue and red nodes) or across layers (green nodes). We note that values of nQ are influenced by, but not entirely dependent on, layer and module assignment. For instance, node 2 undergoes a decrease in nQ from the encmaint to probe layer due to a change in connectivity despite no change in module (green). On the other hand, node 1 undergoes an increase in nQ from the encmaint to probe layer due to increased connectivity within its module while also undergoing a change in module assignment (blue to green). While both node 1 and 2’s changes in modularity are largely driven by changes in connectivity tied to that node, to illustrate how changes in module assignment influence nQ consider node 3. Node 3 undergoes no change in connectivity. However, it observes a decrease in nQ due to an increase in connectivity of node 1. While modularity in general has increased (), the role of node 3 within its module has decreased. It is this interplay between connectivity, modules, and how these change across the layers of the network that influence each node’s nodal contribution to classical multiplex modularity.
Fig 3.
Random and SBM null models for comparisons of modularity in shape and binding tasks.
Above are the violin plots of the modularity distribution for the shape and binding tasks. This is also explored for our random and SBM surrogate networks. Violin plots were generated with publicly available code [61].
Fig 4.
Scatter plots of Degree, PageRank (PR), and Clustering Coefficient (CC) vs. nQ. These measures were calculated in our dual-layer binding task-fMRI networks for cognitively normal subjects. The Pearson correlation between these comparisons is given by r.
Fig 5.
Behaviour of nQ in ZKC and NKI.
Scatter plots of Degree, PageRank (PR), and Clustering Coefficient (CC) vs. nQ for Zachary’s Karate club (a) and NKI-RS (b). r is the Pearson correlation coefficient.
Fig 6.
Changes in nQ for multiplex fMRI binding.
Using BrainNet Viewer, we visualize encmaint and probe (left and right brain respectively) layers of our network. Here, nodes in blue represent loss of nQ while those in red represent gains in nQ. The size of the nodes represent the magnitude of this change, while labelled nodes are those that passed and FDR controlled at
. Labels follow the form task phase (e or p), followed by brain hemisphere (l or r), then a shortened version of the ROI (i.e. LIN refers to the lingual). Refer to Table 2 for a more detailed breakdown of the ROIs present in this figure. Furthermore, only 1.5% of edges are visualized for clarity. Note the magnitude of the gains in nQ for the later stage disease comparisons.
Table 2.
fMRI multiplex binding for controls vs. MCI converters. This table displays the ROIs which passed and where p-values are controlled by FDR at
. These ROIs reside in either the encmaint (EM) or probe (P) layers of our multiplex network and in left (L) or right (R) hemispheres of the brain. Standard p-value and effect size is displayed following permutation test and the area under the curve (AUC) of the Receiver Operating Characteristic (ROC).
Fig 7.
Changes in nQ for single-layer DTI.
As before, blue indicates a loss of nQ while red represents a gain. Here we also see an increase (node size) in nQ for later stage disease comparisons. Furthermore, 1.5% of the network edges are displayed for clarity. Additionally, to reiterate, the DTI networks are constructed based on white matter microstructure. As such, we have a static (no temporal element like in the fMRI networks) single-layer network in this case where the single layer of the network is represented visually by one brain.
Table 3.
DTI for control vs. disease. This table displays the ROIs which passed the thresholds of and where the p-values were FDR controlled at
. L and R indicate the left or right hemispheres of the brain respectively. Standard p-value and effect size is displayed following permutation test and the area under the curve (AUC) of the Receiver Operating Characteristic (ROC).
Fig 8.
Comparisons of nQ for early MCI vs. MCI and MCI vs. MCI converters for single-layer DTI.
Figure generation and details follow from Fig 7. a) Here note the large (node size) increase (red nodes) in nQ in the parietal lobe.