Predicting MEG brain functional connectivity using microstructural information

Abstract

Understanding the role that human brain microstructure plays in the formation of functional connectivity is an important endeavor that could inform therapies for patients with neurological disorders. In this work, magnetic resonance imaging data from ninety healthy participants were used to perform tractography and calculate structural connectivity matrices, using four microstructural metrics to assign connection strength: number of streamlines, fractional anisotropy, radial diffusivity and a myelin measure (derived from the myelin water fraction computed from multi-component relaxometry). Unweighted binarized structural connectivity matrices, marking with 1 existing structural connections and with 0 non-existing ones, and assigning no specific strength to the connections, were also constructed. Magnetoencephalography resting-state data from the same participants were used to calculate their functional connectivity matrices, via correlations of the Hilbert envelopes of the beamformer time series, at four frequency bands: delta (1 – 4 Hz), theta (3 – 8 Hz), alpha (8 – 13 Hz) and beta (13 – 30 Hz). Non-negative matrix factorization (NMF) was then performed to identify the components of the observed resting-state functional connectivity in those bands. Shortest-path-length and search-information analyses of the structural connectomes were used to predict functional connectivity patterns for both the total resting-state functional connectivity and its NMF components.The observed functional connectivity was stronger for brain areas that were closer to each other. The microstructurally-informed algorithms predicted the NMF-derived components of the observed functional connectivity more accurately than they predicted the total (non-factorized) observed functional connectivity. The shortest-path-length algorithm outperformed the searchinformation algorithm in terms of prediction accuracy. When comparing the weights of the structural connectivity matrices, the number of streamlines and the myelin measure gave the most accurate predictions of functional connectivity, while the fractional anisotropy consistently performed poorly. Overall, different structural metrics paint very different pictures of the human structural connectome, of its relationship to functional connectivity, and of which white matter tracts are implicated in the communication between brain areas.

Increased structural connectivity in high schizotypy

“…Using the inverse radial diffusivity to assign strength to the structural connections relates them to the myelination and the axonal density of the white matter tracts, which are measures that better reflect efficient brain communication than the more frequently used number of streamlines or fractional anisotropy (Messaritaki et al, 2021). One limitation of our study is the fact that the schizotypy scores went only up to 43, when the maximum possible value of the SPQ total score is 74.…”

“…In this study we used tractography-derived structural brain networks to investigate possible differences in the organisation of the brain networks of healthy participants with varying schizotypy scores. Various microstructural metrics, such as the number of reconstructed streamlines or the fractional anisotropy, have been used in past studies to assign connectivity strength to structural network edges (Caeyenberghs et al, 2016;Messaritaki et al, 2021;Messaritaki et al, 2019). In this study, in order to capture the possible impact of altered myelination or axonal density on the strength of the connections, we used the inverse radial diffusivity to assign edge weights.…”

Dementia risk factors modify hubs but leave other connectivity measures unchanged in asymptomatic individuals: a graph theoretical analysis

“…Our findings would benefit from replication in a larger sample due to the fragmentation of the initial sample into subgroups with the different risk profiles. It would also be beneficial for structural network analyses to include measures which are believed to play a more important role in the functional performance of the brain, such as myelination of the white matter tracts (Messaritaki et al, 2021) and axonal diameter. We finally note that the thresholding of structural connectivity matrices derived from tractography is still issue of debate.…”

“…The above-mentioned metrics are chosen because they could reflect the signal transport and integration abilities of the structural connectome. However, it is not clear yet to what extent they achieve that (Messaritaki et al, 2020). Additionally, the strength of the structural connectivity between brain areas depends on the metric used to weight the network edges.…”