Edge-centric analysis of stroke patients: An alternative approach for biomarkers of lesion recovery

Idesis, Sebastian; Faskowitz, Joshua; Betzel, Richard F.; Corbetta, Maurizio; Sporns, Olaf; Deco, Gustavo

doi:10.1016/j.nicl.2022.103055

Cited by 25 publications

(17 citation statements)

References 65 publications

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“…Previous studies discussed the role of graph theory metrics in stroke patients ( Han et al, 2020 , Idesis et al, 2022 , Sun et al, 2021 , Vecchio et al, 2019b ) revealing how properties such as global efficiency indicate the efficiency of integration of distributed information through the whole network. Nevertheless, the studies relied on FC to calculate the corresponding metrics.…”

Section: Discussionmentioning

confidence: 99%

Inferring the dynamical effects of stroke lesions through whole-brain modeling

Idesis

Favaretto

Metcalf

et al. 2022

NeuroImage: Clinical

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Section: Discussionmentioning

confidence: 99%

Inferring the dynamical effects of stroke lesions through whole-brain modeling

Idesis

Favaretto

Metcalf

et al. 2022

NeuroImage: Clinical

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“…Therefore, while lesions in, for example, the precuneus and the superior frontal cortex are both single-node in our simulations, in reality, the latter would involve a much larger volume. Nonetheless, while studies show that lesion volume has an impact on the extent of functional damage and subsequent recovery (121), it is arguable that the graph properties of lesioned areas have a significant influence as well (60,122). Also, given the heterogeneity in the lesions applied in this study, we argue that it still retains validity in representing the wide range of post-stroke deficits and the participation of E-I homeostasis in recovery.…”

Section: Discussionmentioning

confidence: 99%

Multiscale Effects of Excitatory-Inhibitory Homeostasis in Lesioned Cortical Networks: A Computational Study

Santos

Vohryzek

Verschure

2022

Preprint

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Stroke-related disruptions in functional connectivity (FC) often spread beyond lesioned areas and, given the localized nature of lesions, it is unclear how the recovery of FC is orchestrated on a global scale. Since recovery is accompanied by long-term changes in excitability, we propose excitatory-inhibitory (E-I) homeostasis as a driving mechanism. We present a large-scale model of the neocortex, with synaptic scaling of local inhibition, showing how E-I homeostasis can drive the post-lesion restoration of FC and linking it to changes in excitability. We show that functional networks could reorganize to recover disrupted modularity and small-worldness, but not network dynamics, suggesting the need to consider forms of plasticity beyond synaptic scaling of inhibition. On average, we observed widespread increases in excitability, with the emergence of complex lesion-dependent patterns related to biomarkers of relevant side effects of stroke, such as epilepsy, depression and chronic pain. In summary, our results show that the effects of E-I homeostasis extend beyond local E-I balance, driving the restoration of global properties of FC, and relating to post-stroke symptomatology. Therefore, we suggest the framework of E-I homeostasis as a relevant theoretical foundation for the study of stroke recovery and for understanding the emergence of meaningful features of FC from local dynamics.

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“…Inspired by applications in network science [61,62], several recent studies have proposed "edge-centric" approaches for defining and characterizing the brain's system-level architecture [30,[63][64][65]. Unlike traditional methods for defining brain network systems, the "edgecentric" approach assigns cluster labels at the level of edges so that, from the perspective of each node, it maintains an affiliation to a plurality of clusters.…”

Section: Movies Decouple Sensorimotor Systems From One Anothermentioning

confidence: 99%

System-level high-amplitude co-fluctuations

Betzel

Chumin

Esfahlani

et al. 2022

Preprint

Self Cite

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Edge time series decompose interregional correlations (functional connectivity; FC) into their time-varying contributions. Previous studies have revealed that brief, high-amplitude, and globally-defined "events" contribute disproportionately to the time-averaged FC pattern. This whole-brain view prioritizes systems that occupy vast neocortical territory, possibly obscuring extremely high-amplitude co-fluctuations that are localized to smaller brain systems. Here, we investigate local events detected at the system level, assessing their independent contributions to global events and characterizing their repertoire during resting-state and movie-watching scans. We find that, as expected, global events are more likely to occur when large brain systems exhibit events. Next, we study the co-fluctuation patterns that coincide with system events--i.e. events detected locally based on the behavior of individual brain systems. We find that although each system exhibits a distinct co-fluctuation pattern that is dissimilar from those associated with global events, the patterns can nonetheless be grouped into two broad categories, corresponding to events that coincide with sensorimotor and attention systems and, separately, association systems. We then investigate system-level events during movie-watching, discovering that the timing of events in sensorimotor and attention systems decouple, yielding reductions in co-fluctuation amplitude. Next, we show that by associating each edge with its most similar system-averaged edge time series, we recover overlapping community structure, obviating the need for applying clustering algorithms to high-dimensional edge time series. Finally, we focus on cortical responses to system-level events in subcortical areas and the cerebellum. We show that these structures coincide with spatially distributed cortical co-fluctuations, centered on prefrontal and somatosensory systems. Collectively, the findings presented here help clarify the relative contributions of large and small systems to global events, as well as their independent behavior.

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Edge-centric analysis of stroke patients: An alternative approach for biomarkers of lesion recovery

Cited by 25 publications

References 65 publications

Inferring the dynamical effects of stroke lesions through whole-brain modeling

Inferring the dynamical effects of stroke lesions through whole-brain modeling

Multiscale Effects of Excitatory-Inhibitory Homeostasis in Lesioned Cortical Networks: A Computational Study

System-level high-amplitude co-fluctuations

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