Linking Entropy at Rest with the Underlying Structural Connectivity in the Healthy and Lesioned Brain

Abstract

The brain is a network that mediates information processing through a wide range of states. The extent of state diversity is a reflection of the entropy of the network. Here we measured the entropy of brain regions (nodes) in empirical and modeled functional networks reconstructed from resting state fMRI to address the connection of entropy at rest with the underlying structure measured through diffusion spectrum imaging. Using 18 empirical and 18 modeled stroke networks, we also investigated the effect that focal lesions have on node entropy and information diffusion. Overall, positive correlations between node entropy and structure were observed, especially between node entropy and node strength in both empirical and modeled data. Although lesions were restricted to one hemisphere in all stroke patients, entropy reduction was not only present in nodes from the damaged hemisphere, but also in nodes from the contralesioned hemisphere, an effect replicated in modeled stroke networks. Globally, information diffusion was also affected in empirical and modeled strokes compared with healthy controls. This is the first study showing that artificial lesions affect local and global network aspects in very similar ways compared with empirical strokes, shedding new light into the functional nature of stroke.

Advanced lesion symptom mapping analyses and implementation asBCBtoolkit

“…altered properties of the vasculature) could also be captured by measures of rs-fMRI entropy. Our results replicated recently published results, showing a strong decrease of entropy in both hemispheres when patients were compared to controls [100]. This indicates a large-scale effect of brain lesion on the overall blood oxygen level dependent dynamic of the brain.…”

Structural disconnections explain brain network dysfunction after stroke

“…Our results are consistent with a general prediction of previous computational studies modeling lesion-induced FC disruptions -namely that a lesion's impact on FC is strongly influenced by its expected impact on the structural connectome (Alstott et al, 2009;Cabral et al, 2012;Saenger et al, 2017;Váša et al, 2015). We note, however, that a drawback of most computational studies is that they have typically simulated SDCs by node-wise edge deletion for damaged nodes, and this approach cannot account for the effects of WM damage, which is common (Fig.…”

“…Given that structural connectivity (SC) both directly and indirectly shapes FC in the healthy brain (Adachi et al, 2011;Goni et al, 2014;Greicius et al, 2009;Van Den Heuvel et al, 2009;Honey and Sporns, 2009), we expected that structural disconnection (SDC) plays a similarly fundamental role in determining the severity of FC disruptions caused by stroke. While this expectation aligns with predictions based on simulation studies (Alstott et al, 2009;Cabral et al, 2012;Saenger et al, 2017), empirical support is scarce (Carter et al, 2012) and largely based on evidence from callosal resections and traumatic brain injuries (Jilka et al, 2014;Johnston et al, 2008;Roland et al, 2017). We therefore aimed to test the hypothesis that a stroke's distributed impact on the structural connectome, not its focal impact on critical FC network nodes, is what determines its impact on FC.…”

Lesion Quantification Toolkit: A MATLAB software tool for estimating grey matter damage and white matter disconnections in patients with focal brain lesions

“…Accordingly, similar matrix-based representations of parcel-to-parcel white matter (dis)connectivity are commonly employed by studies investigating region-level structurefunction relationships in the healthy brain (Adachi et al, 2011;Goni et al, 2014;Honey et al, 2007), and we have also recently employed parcel-wise disconnection matrices to investigate how the interregional functional connectivity disruptions observed after stroke relate to inter-regional white matter disconnections (Griffis et al, 2020(Griffis et al, , 2019. In addition, because they provide a parametrizable representation of the full structural (dis)connectome, matrix-based representations of white matter (dis)connectivity are also commonly employed by studies modeling the expected effects of brain lesions on the structural and functional connectomes (Alstott et al, 2009;Cabral et al, 2012;Saenger et al, 2017). Relatedly, it is worth noting that while previous lesion-connectome modeling studies have often simulated the effects of lesions on the structural connectome using parcel-wise or random connection deletion approaches, these simulation approaches cannot account for the fact that real lesions often damage the white matter and cause correlated disconnections among regions that are located within the same vascular territory (e.g.…”