Lucas Breedt1, Fernando Santos1, Arjan Hillebrand2, Liesbeth Reneman1, Anne-Fleur van Rootselaar1, Menno Schoonheim2, Cornelis Stam2, Anouk Ticheler1, Betty Tijms3, Dick Veltman1, Chris Vriend1, Margot Wagenmakers4, Guido van Wingen1, Jeroen Geurts2, Anouk Schrantee1, Linda Douw2
1Amsterdam UMC, Amsterdam, Noord-Holland, 2Amsterdam University Medical Center, Amsterdam, NETHERLANDS, 3Alzheimer Center Amsterdam, VUmc, Amsterdam, Netherlands, 4GGZ inGeest, Amsterdam, Noord-Holland
Amsterdam University Medical Center
Alzheimer Center Amsterdam, VUmc
Amsterdam University Medical Center
Amsterdam University Medical Center
In recent years, the field of neuroscience has shifted towards a network view of the brain, and it is understood that cognitive processes like executive functioning (EF) are dependent on a network organization that facilitates both segregation and integration (Watts 1998). This organization tends to become more efficient during development, after which it regresses to less optimal topologies in later life (Hwang 2013; Onoda 2013). Brain regions that are highly central within the network, also called hubs, seem crucial in facilitating network integration (Bertolero 2013). The frontoparietal network (FPN) contains many such hubs and has been shown to play an essential role in EF (Marek 2018). However, brain networks can be constructed using different modalities: diffusion MRI (dMRI) can be used to obtain structural networks, while resting-state fMRI (rsfMRI) and EEG/MEG yield functional networks. These networks are often studied in isolation, but give complementary information that should perhaps be considered simultaneously (Garcés 2016). Multilayer network analysis allows for integration of different modalities into one 'network of networks' (Boccaletti 2014). This representation may contribute to a better understanding of the brain, but the relation between multilayer network properties and healthy cognition and aging has not yet been investigated. We thus hypothesize that individual differences in multilayer network centrality of the FPN explains more variance in EF than monolayer centralities of the FPN.
We obtained dMRI, rsfMRI, MEG, and neuropsychological assessments in 33 healthy volunteers (55% female, age 20-70 years). EF was defined by averaging age- and education-corrected z-scores of animal fluency, Stroop (interference condition), and concept shifting tests. All imaging data were registered to a 3DT1-weighted image and parcellated according to the Brainnetome atlas. We performed probabilistic anatomically-constrained tractography in MRtrix3 to construct weighted structural networks from the dMRI data. Pre-processing of the rsfMRI and MEG data was performed as described previously (Nissen 2017; Tijhuis 2020). To obtain weighted functional networks, we calculated Pearson correlations between rsfMRI time-series and the phase lag index between all pairs of regions in six MEG frequency bands (delta-gamma). We then constructed the minimum spanning tree (MST) of all networks. We used weighted dMRI and rsfMRI networks and MEG MSTs for the monolayer analyses, and integrated the MSTs of all modalities to construct multiplex networks. The weights of all interlayer links were set to 1 (Fig. 1). Finally, we computed nodal eigenvector centrality and averaged the centralities of the FPN nodes. We used hierarchical multiple regression to explore the added value of multilayer centrality over monolayer FPN centralities in explaining EF. Additionally, we used hierarchical multiple regression to assess the relation between healthy aging and multilayer FPN centrality.
EF was normally distributed (M=0.297, SD=0.613). Interestingly, visual exploration of the network data revealed that multilayer hubs presented themselves in different regions compared to monolayer hubs. Indeed, higher multilayer centrality of the FPN, but not monolayer centrality, related to better EF (adj. R2=.105, p=.037). Also, we observed the expected quadratic relation between age and multilayer FPN centrality (adj. R2=.241, p=.006); see Fig. 2.
Our results suggest that individual differences in multimodal, multilayer FPN centrality explain more variance in EF than monolayer FPN centrality. Furthermore, multilayer FPN centrality increases with age, peaks around middle age, and then declines. This possibly reflects the rise and fall of the efficiency of the brain network across the life span. Overall, our study provides evidence that integrating multimodal information through a multilayer framework may help advance our knowledge of the neural correlates of cognition.
Higher Cognitive Functions:
Executive Function, Cognitive Control and Decision Making 2
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural)
Other Methods 1
Other - Multilayer networks
1|2Indicates the priority used for review
My abstract is being submitted as a Software Demonstration.
Please indicate below if your study was a "resting state" or "task-activation” study.
Healthy subjects only or patients (note that patient studies may also involve healthy subjects):
Was any human subjects research approved by the relevant Institutional Review Board or ethics panel?
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Bertolero MA. The diverse club. Nature communications. 2017;8(1):1-11. doi:10.1038/s41467-017-01189-w
Boccaletti S. The structure and dynamics of multilayer networks. Physics Reports. 2014;544(1):1-122. doi:10.1016/j.physrep.2014.07.001
Garcés P. Multimodal description of whole brain connectivity: A comparison of resting state MEG, fMRI, and DWI. Human brain mapping. 2016;37(1):20-34. doi:10.1002/hbm.22995
Hwang K. The development of hub architecture in the human functional brain network. Cerebral Cortex. 2013;23(10):2380-2393. doi:10.1093/cercor/bhs227
Marek S. The frontoparietal network: function, electrophysiology, and importance of individual precision mapping. Dialogues in clinical neuroscience. 2018;20(2):133. doi:10.31887/dcns.2018.20.2/smarek
Nissen IA. Identifying the epileptogenic zone in interictal resting‐state MEG source‐space networks. Epilepsia. 2017;58(1):137-148. doi:10.1111/epi.13622
Onoda K. Small-worldness and modularity of the resting-state functional brain network decrease with aging. Neuroscience letters. 2013;556:104-108. doi:10.1016/j.neulet.2013.10.023
Tijhuis FB. Dynamic functional connectivity as a neural correlate of fatigue in multiple sclerosis. NeuroImage: Clinical. 2020:102556. doi:10.1016/j.nicl.2020.102556
Watts DJ. Collective dynamics of ‘small-world’networks. nature. 1998;393(6684):440. doi:10.1038/30918