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Martijn P. van den Heuvel
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Journal Articles
Publisher: Journals Gateway
Network Neuroscience (2019) 3 (4): 1094–1120.
Published: 01 September 2019
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Whole-brain computational modeling based on structural connectivity has shown great promise in successfully simulating fMRI BOLD signals with temporal coactivation patterns that are highly similar to empirical functional connectivity patterns during resting state. Importantly, previous studies have shown that spontaneous fluctuations in coactivation patterns of distributed brain regions have an inherent dynamic nature with regard to the frequency spectrum of intrinsic brain oscillations. In this modeling study, we introduced frequency dynamics into a system of coupled oscillators, where each oscillator represents the local mean-field model of a brain region. We first showed that the collective behavior of interacting oscillators reproduces previously shown features of brain dynamics. Second, we examined the effect of simulated lesions in gray matter by applying an in silico perturbation protocol to the brain model. We present a new approach to map the effects of vulnerability in brain networks and introduce a measure of regional hazardousness based on mapping of the degree of divergence in a feature space. Author Summary Computational modeling of the brain enables us to test different hypotheses without any experimental complication, and it provides us with a platform for improving our understanding of different brain mechanisms. In this study, we proposed a new macroscopic computational model of the brain oscillations for resting-state fMRI. Optimizing model parameters using empirical data was performed based on several measures of functional connectivity and instantaneous coherence. We simulated the effect of malfunction in a brain region by changing that region’s dynamics to evoke noisy behavior. Together with presenting a new paradigm for local vulnerability mapping in the brain connectome, we evaluated the hazard rate induced after perturbing a brain region by measuring divergence of the perturbed model from the original model in feature space. The analysis of hazard rates induced by primary failures of individual brain regions provides relevant insights not only into the size of the damage inflicted on the connectome by a particular failure, but also into the potential origins of disease. Furthermore, we proposed a spatial brain map that is associated with the regional hazardousness rates, which is in good agreement with the known pathophysiologic roles of malfunction in different functional systems in the brain.
Includes: Supplementary data
Journal Articles
Connection strength of the macaque connectome augments topological and functional network attributes
Publisher: Journals Gateway
Network Neuroscience (2019) 3 (4): 1051–1069.
Published: 01 September 2019
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Author Summary High-resolution tract-tracing methods allow for the measurement of cortico-cortical anatomical pathways and quantification of their connectivity strength. The variable strength of neural pathways holds additional information on the complex organization of brain networks. We studied connectivity strength in the macaque by using the open-access connectome dataset of Markov and colleagues (2014), which contains high-resolution information on the number of axonal projections interconnecting brain regions. Our results show that connectivity strength adopts a nonrandom distribution and enhances key network features already present in the binary version of the network. Using the Kuramoto model to simulate functional dynamics, we further demonstrated that connectivity strength enhances local processing and global network integration. This study aids in the understanding of the macaque connectome by clarifying the role of connectivity strength in the network topology and functioning. Abstract Mammalian brains constitute complex organized networks of neural projections. On top of their binary topological organization, the strength (or weight) of these neural projections can be highly variable across connections and is thus likely of additional importance to the overall topological and functional organization of the network. Here we investigated the specific distribution pattern of connection strength in the macaque connectome. We performed weighted and binary network analysis on the cortico-cortical connectivity of the macaque provided by the unique tract-tracing dataset of Markov and colleagues (2014) and observed in both analyses a small-world, modular and rich club organization. Moreover, connectivity strength showed a distribution augmenting the architecture identified in the binary network version by enhancing both local network clustering and the central infrastructure for global topological communication and integration. Functional consequences of this topological distribution were further examined using the Kuramoto model for simulating interactions between brain regions and showed that the connectivity strength distribution across connections enhances synchronization within modules and between rich club hubs. Together, our results suggest that neural pathway strength promotes topological properties in the macaque connectome for local processing and global network integration.
Includes: Supplementary data
Journal Articles
Publisher: Journals Gateway
Network Neuroscience (2018) 3 (1): 124–137.
Published: 01 December 2018
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The human brain comprises an efficient communication network, with its macroscale connectome organization argued to be directly associated with the underlying microscale organization of the cortex. Here, we further examine this link in the human brain cortex by using the ultrahigh-resolution BigBrain dataset; 11,660 BigBrain profiles of laminar cell structure were extracted from the BigBrain data and mapped to the MRI based Desikan–Killiany atlas used for macroscale connectome reconstruction. Macroscale brain connectivity was reconstructed based on the diffusion-weighted imaging dataset from the Human Connectome Project and cross-correlated to the similarity of laminar profiles. We showed that the BigBrain profile similarity between interconnected cortical regions was significantly higher than those between nonconnected regions. The pattern of BigBrain profile similarity across the entire cortex was also found to be strongly correlated with the pattern of cortico-cortical connectivity at the macroscale. Our findings suggest that cortical regions with higher similarity in the laminar cytoarchitectonic patterns have a higher chance of being connected, extending the evidence for the linkage between macroscale connectome organization and microscale cytoarchitecture. Author Summary The human brain connectome organization has been suggested to associate with cytoarchitecture similarity. Here, we utilize the state-of-the-art ultrahigh-resolution BigBrain dataset and diffusion-weighted imaging dataset to examine this association. Our results show that cortical regions with higher cytoarchitecture similarity are more likely to be connected, as well as connected by stronger white matter tracts. This work further extends our understanding of the interaction between macroscale cortico-cortical connectivity organization and microscale cortical cytoarchitecture.
Includes: Supplementary data