Modeling the outcome of structural disconnection on resting-state functional connectivity
Highlights
► Resting brain model of neural populations coupled through anatomical pathways. ► Healthy resting-state BOLD functional connectivity reproduced by the model. ► Graph properties of simulated functional networks match healthy data. ► Structural disconnection modeled as global or local coupling decrease. ► Disconnection leads to altered functional networks as in schizophrenia.
Introduction
The spatial patterns observed in brain activity during rest are thought to be shaped by the underlying anatomical structure (Bullmore and Sporns, 2009, Jirsa et al., 2010, Skudlarski et al., 2008). The availability of whole-brain maps of anatomical connections (Hagmann et al., 2008, Kötter, 2004, Sporns et al., 2005) together with computational models of the brain's large-scale neural dynamics have shed light on the relationship between anatomical and functional connectivity (Cabral et al., 2011, Deco et al., 2009, Ghosh et al., 2008, Honey et al., 2007, Honey et al., 2009). Importantly, models can be used to predict the effects of structural alterations on brain dynamics (Alstott et al., 2009, Honey and Sporns, 2008), which is beyond reach on the experimental side, making models a unique tool for the comprehension of brain diseases.
Brain networks have been widely studied by means of graph theory (Bullmore and Sporns, 2009, Rubinov and Sporns, 2010), whether derived from white-matter connections (anatomical networks) or from temporal correlations (functional networks) between brain areas. The application of graph theoretical measures to functional networks derived from blood oxygenation level-dependent (BOLD) signals measured using functional magnetic resonance imaging (fMRI) during rest has shown clinical relevance. Indeed, this procedure has revealed significant alterations in the resting-state patterns of patients with neuropathologies such as schizophrenia (Bassett et al., 2012, Liu et al., 2008, Lynall et al., 2010) and Alzheimer's disease (Supekar et al., 2008), among others.
In this study, we focus on the effects of a structural disconnection on resting-state functional networks. Resting-state functional connectivity is investigated using a model of large-scale ongoing brain neural activity. The model consists of local neural populations dynamically coupled via white-matter anatomical pathways. The coupling weights between neural populations scale the long-distance excitatory strength between brain regions considering simultaneously two factors: 1) the number of white matter fiber tracts detected between those regions using DTI/DSI tractography and 2) the excitatory synaptic weights. From the simulated ongoing brain activity, we estimated the hemodynamic response and computed functional connectivity matrices. Subsequently, simulated functional networks were characterized using graph theory following the methodology from Lynall et al. (2010) for a reliable comparison. In a first step, using a healthy anatomical connectome, we study how the topological organization of simulated functional networks depends on the global structural coupling strength. For a range of coupling strengths, the simulated functional networks were found to have graph properties similar to the ones reported for healthy controls in the work of Lynall et al. (2010). Subsequently, the effects of a pathological disconnection were simulated in two different ways: either by globally decreasing the coupling strength, or by randomly pruning anatomical connections. Theoretical results indicate that, in the model, all disconnections should induce the same qualitative changes.
Schizophrenia is a disorder which has been hypothesized to be related with disconnection effects, and so we have compared our results with experimental measures from schizophrenia patients (Lynall et al., 2010). We found that the reorganization of resting-state functional networks observed between healthy volunteers and people with schizophrenia can be explained by a structural disconnection, both schemes leading to similar results. Overall, these results support the hypothesis that the functional network alterations underlying schizophrenia are caused by a disconnection (encompassing putative local/global axonal/synaptic mechanisms), in agreement with current theories of schizophrenia (Bullmore et al., 1997, Friston and Frith, 1995, Skudlarski et al., 2010, Stephan et al., 2006, Wernicke, 1906, Winterer and Weinberger, 2004, Zalesky et al., 2011). Taken beyond the schizophrenia disorder, our results could provide a new light towards the understanding of altered resting-state functional connectivity occurring in other mental illnesses characterized by disconnection.
Section snippets
Anatomical connectivity
The brain's anatomical connectivity (AC)—or connectome—is defined as the map of neural connections in the brain. In low-resolution maps such as the ones used here, nodes correspond to segregated brain regions and links are derived from the white matter anatomical pathways interconnecting them. These networks have shown to be a key ingredient to models of resting-state functional connectivity (Cabral et al., 2011, Honey et al., 2009). Given that results can be influenced by the parcellation
Results
Overall, we found that the simulated functional networks generated with the neurodynamical model specified in Neural Dynamics Model depend largely on the underlying structural connectivity. First, using a healthy AC we studied how parameters such as correlation strength, global integration and a number of measures from graph theory, vary as a function of the global coupling strength k. This coupling strength, encompassing axonal and synaptic mechanisms, uniformly scales all the connection
Discussion
In the present work, we used a modeling approach to investigate the role of structural connectivity in shaping functional networks as measured with fMRI during rest. Structural connectivity is ensured by brain mechanisms involved in long-range signal transmission in the brain, including axonal connectivity (dependent on the number, density and coherence of axon fibers) and synaptic mechanisms (e.g. neurotransmission and plasticity). As shown here, a disruption of these mechanisms, at either a
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