Pilot part 3: Constructing a two-layer network of rsMEG and rsfMRI 

What did we do?

For subject P022, we constructed a multilayer consisting of both rsMEG and rsfMRI intralayer connectivity, as well as the interlayer connectivity between them:

supramatrix_fmri_MEG_4nodes_v2 .pngIt is evident that the rsfMRI intralayer connections (upper left matrix) and rsMEG intralayer connections (lower right matrix) have very different raw values: an uncorrected Pearson Correlation tends to be quite high, while the Phase Lag Index generally varies between 0.2-0.3. Regardless, it is interesting to notice the interlayer connections between rsfMRI and rsMEG, which are weak but do vary according to node. Many issues need to be addressed though, before we can draw any conclusions from this type of analysis (window lengths, normalization of connectivity weights before we do graph analyses, etc, etc).

Pilot part 2: Constructing multilayer networks for both rsMEG and rsfMRI separately 

What did we do?

Results show patterns of connectivity occurring over different timescales for both rsMEG and rsfMRI, as we would expect. The four nodes depicted for clarity include the left and right posterior cingulate cortex (PCC), and the left and right Medial Frontal Gyrus (MFG). This is the complete rsfMRI supra-adjacency matrix for subject P022, which consists of 36 layers (each is 45s long):

Multiplex_win45sec_10s_shift_36windows_4nodes_HCGE_P022As we may expect, the diagonal of this supra-adjacency matrix shows the highest correlations, as these are the within-window connectivity patterns of the four nodes. However, we may also observe a variety of stronger connectivity between activity patterns of a specific node across different windows (layers).

The same goes for the following rsMEG supra-adjacency matrix of the same subject, consisting of 22 layers (windows) of 12 seconds each.

PLI_supra_adj_win12sec_no-overlap_22windows_P022

However, these matrices are large and difficult to read – multilayer graph properties will be able to assess dynamics of these massive datasets in  a data-driven way. For now, in order to preliminarily assess congruence between unimodal multilayers, the following figures contain only 3 layers per modality (rsfMRI on the left, rsMEG on the right) for the same four nodes in P022.

We may observe several commonalities, such has the relatively high connectivity between the LPCC and RPCC (both part of the Default Mode Network), or the general lowering of interlayer connectivity between layers 1 and 3 as compared to 1 and 2, likely since these windows are further apart in time. However, we also see marked differences between rsMEG and rsfMRI multilayers, particularly with respect to intralayer connectivity between the LMFG and RMFG, which is very high in rsfMRI and very low in rsMEG. These types of modality-dependent connectivities may strongly inform the multimodal multilayer properties that TIMNET aims to pick up on, while separate interpretation of these networks could yield contrasting and uninterpretable results.

Another examplar set of rsfMRI (left) and rsMEG (right) multilayers is shown for subject P033:

In order to begin to assess whether these multilayers indeed represent an individual signature of brain organization (and ultimately cognition), we calculated multilayer efficiency (i.e. the weight of all shortest paths in the network, taking both intralayer and interlayer connections into account) for each modality in each subject. When ranking these values, we notice that 3/5 subjects have the same rank for their rsMEG and rsfMRI multilayer.

Pilot part 3: Constructing a two-layer network of rsMEG and rsfMRI

TIMNET: pilot data

Pilot part 1: Reproducing individual experimental rsMEG unilayer network with a network of coupled Jansen-rit units based on the individual anatomical network 

What did we do?

After using the Phase Locking Value to create connectivity matrices for both real and modeled time series in each subject, we determined how much the resulting two matrices were spatially alike per subject. This correlation between the individual experimental and modeled rsMEG network was highly significant (range Rho 0.250–0.428, all p<0.0001). The two subjects with the lowest correlations are exemplified below.

Healthy control P033: spatial correlation between real (left) and modeled (right) MEG is 0.250 (p<0.0001). Each matrix consists of 78 cortical regions and the Phase Locking Values between them.

Healthy control P062: spatial correlation 0.282 (p<0.0001):

Pilot part 2: Constructing multilayer networks for both rsMEG and rsfMRI separately 

Pilot part 3: Constructing a two-layer network of rsMEG and rsfMRI 

Quirien Oort

Quirien Oort is no stranger in neuro-oncological research: coming from a neuropsychological background, she is been working on her dissertation on quality of life in brain tumor patients with Martin Taphoorn (LUMC), Linda Dirven (LUMC), and Jaap Reijneveld (VUmc). She now joins the lab to explore MRI and network analysis. Welcome Quirien!

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Spotlight on Neurology paper

The paper on hub-rich networks in MS by Kim Meijer was put in the spotlight by the editors of Neurology. In this study, cognitive impairment in multiple sclerosis was related to altered communication of hub-rich networks. The default mode and frontoparietal networks showed increased functional connectivity, but only with more peripheral regions (i.e., non-hubs), in cognitively impaired patients. This seemingly negative shift in network balance potentially underlies cognitive dysfunction in multiple sclerosis.

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OHBM 2017

We will be going to Vancouver, and will present the following posters:

  • Tuesday, #2019: the microscopic substrates of macro-scale graph theoretical properties of the brain, first author Svenja Kiljan (more info here)
  • Thursday, #3057: the (dynamic) connectivity profile of the cerebellum in MS, and its cognitive correlates, first author Menno Schoonheim (more info here)
  • Thursday, #3061: the dynamic connectivity mechanisms underlying content-specific memory functioning in MS, first author Quinten van Geest (more info here)

Hope to have some great discussions over these investigations!