Brain Network Evolution after Stroke Based on Computational Experiments

Li, Wei; Huang, Yue; Li, Yapeng; Chen, Xi

doi:10.1371/journal.pone.0082845

Cited by 12 publications

(11 citation statements)

References 51 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Small-worldness reflects an optimal network structure associated with rapid synchronization and information transfer, minimal wiring costs, as well as a balance between local processing and global integration ( Watts and Strogatz, 1998 ). However, it is still not clear whether clinical recovery after stroke is paralleled by a decreased small-world organization of the brain (or parts of it), as suggested by some studies ( Wang et al, 2010 ; Caliandro et al, 2017 ) or, on the contrary, by an increased small-worldness, as suggested by others ( Tsirka et al, 2011 ; De Vico Fallani et al, 2012 ; Li et al, 2013 ). Several factors may in fact influence the evolution of the small-world parameter, such as patient cohort, phase of stroke, lesion type, brain areas investigated, brain signals (e.g., fMRI or EEG), band selected, etc.…”

Section: Discussionmentioning

confidence: 97%

“…Previous works have shown that brain networks of patients in both acute and subacute phases of stroke present rearrangements with respect to controls, which could be detected by measuring the network small-worldness ( Wang et al, 2010 ; Tsirka et al, 2011 ; De Vico Fallani et al, 2012 ; Li et al, 2013 ; Caliandro et al, 2017 ). Small-worldness reflects an optimal network structure associated with rapid synchronization and information transfer, minimal wiring costs, as well as a balance between local processing and global integration ( Watts and Strogatz, 1998 ).…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Connectivity Measures Differentiate Cortical and Subcortical Sub-Acute Ischemic Stroke Patients

Fanciullacci

Panarese

Spina

et al. 2021

Front. Hum. Neurosci.

View full text Add to dashboard Cite

Brain lesions caused by cerebral ischemia lead to network disturbances in both hemispheres, causing a subsequent reorganization of functional connectivity both locally and remotely with respect to the injury. Quantitative electroencephalography (qEEG) methods have long been used for exploring brain electrical activity and functional connectivity modifications after stroke. However, results obtained so far are not univocal. Here, we used basic and advanced EEG methods to characterize how brain activity and functional connectivity change after stroke. Thirty-three unilateral post stroke patients in the sub-acute phase and ten neurologically intact age-matched right-handed subjects were enrolled. Patients were subdivided into two groups based on lesion location: cortico-subcortical (CS, n = 18) and subcortical (S, n = 15), respectively. Stroke patients were evaluated in the period ranging from 45 days since the acute event (T0) up to 3 months after stroke (T1) with both neurophysiological (resting state EEG) and clinical assessment (Barthel Index, BI) measures, while healthy subjects were evaluated once. Brain power at T0 was similar between the two groups of patients in all frequency bands considered (δ, θ, α, and β). However, evolution of θ-band power over time was different, with a normalization only in the CS group. Instead, average connectivity and specific network measures (Integration, Segregation, and Small-worldness) in the β-band at T0 were significantly different between the two groups. The connectivity and network measures at T0 also appear to have a predictive role in functional recovery (BI T1-T0), again group-dependent. The results obtained in this study showed that connectivity measures and correlations between EEG features and recovery depend on lesion location. These data, if confirmed in further studies, on the one hand could explain the heterogeneity of results so far observed in previous studies, on the other hand they could be used by researchers as biomarkers predicting spontaneous recovery, to select homogenous groups of patients for the inclusion in clinical trials.

show abstract

Section: Discussionmentioning

confidence: 97%

Section: Discussionmentioning

confidence: 99%

Connectivity Measures Differentiate Cortical and Subcortical Sub-Acute Ischemic Stroke Patients

Fanciullacci

Panarese

Spina

et al. 2021

Front. Hum. Neurosci.

View full text Add to dashboard Cite

show abstract

“…For each individual, the brain images ( Fig. 1a ) were parcelled into 90 ROIs by automatic anatomical labelling (AAL) 26 27 28 , as shown in Fig. 1b (the names of the ROIs are provided in the Supplementary materials ).…”

Section: Methodsmentioning

confidence: 99%

Simulating the Evolution of Functional Brain Networks in Alzheimer’s Disease: Exploring Disease Dynamics from the Perspective of Global Activity

Wang

Zhu

et al. 2016

Sci Rep

Self Cite

View full text Add to dashboard Cite

Functional brain connectivity is altered during the pathological processes of Alzheimer’s disease (AD), but the specific evolutional rules are insufficiently understood. Resting-state functional magnetic resonance imaging indicates that the functional brain networks of individuals with AD tend to be disrupted in hub-like nodes, shifting from a small world architecture to a random profile. Here, we proposed a novel evolution model based on computational experiments to simulate the transition of functional brain networks from normal to AD. Specifically, we simulated the rearrangement of edges in a pathological process by a high probability of disconnecting edges between hub-like nodes, and by generating edges between random pair of nodes. Subsequently, four topological properties and a nodal distribution were used to evaluate our model. Compared with random evolution as a null model, our model captured well the topological alteration of functional brain networks during the pathological process. Moreover, we implemented two kinds of network attack to imitate the damage incurred by the brain in AD. Topological changes were better explained by ‘hub attacks’ than by ‘random attacks’, indicating the fragility of hubs in individuals with AD. This model clarifies the disruption of functional brain networks in AD, providing a new perspective on topological alterations.

show abstract

“…So far, only very few models have addressed dynamic changes in network topology after brain lesions. Li et al ( 2013 ) described changes in topology merely phenomenologically and did not include any neuronal mechanism such as the formation and deletion of synapses. Others have applied neural mass models with various rules of plasticity and assessed by graph theoretical methods the changes in inter-area connectivity in response to lesions and degeneration (Rubinov et al, 2009 ; Stam et al, 2010 ; de Haan et al, 2012 ).…”

Section: Discussionmentioning

confidence: 99%

Homeostatic structural plasticity can account for topology changes following deafferentation and focal stroke

Butz

Steenbuck

Ooyen³

2014

Front. Neuroanat.

View full text Add to dashboard Cite

After brain lesions caused by tumors or stroke, or after lasting loss of input (deafferentation), inter- and intra-regional brain networks respond with complex changes in topology. Not only areas directly affected by the lesion but also regions remote from the lesion may alter their connectivity—a phenomenon known as diaschisis. Changes in network topology after brain lesions can lead to cognitive decline and increasing functional disability. However, the principles governing changes in network topology are poorly understood. Here, we investigated whether homeostatic structural plasticity can account for changes in network topology after deafferentation and brain lesions. Homeostatic structural plasticity postulates that neurons aim to maintain a desired level of electrical activity by deleting synapses when neuronal activity is too high and by providing new synaptic contacts when activity is too low. Using our Model of Structural Plasticity, we explored how local changes in connectivity induced by a focal loss of input affected global network topology. In accordance with experimental and clinical data, we found that after partial deafferentation, the network as a whole became more random, although it maintained its small-world topology, while deafferentated neurons increased their betweenness centrality as they rewired and returned to the homeostatic range of activity. Furthermore, deafferentated neurons increased their global but decreased their local efficiency and got longer tailed degree distributions, indicating the emergence of hub neurons. Together, our results suggest that homeostatic structural plasticity may be an important driving force for lesion-induced network reorganization and that the increase in betweenness centrality of deafferentated areas may hold as a biomarker for brain repair.

show abstract

Brain Network Evolution after Stroke Based on Computational Experiments

Cited by 12 publications

References 51 publications

Connectivity Measures Differentiate Cortical and Subcortical Sub-Acute Ischemic Stroke Patients

Connectivity Measures Differentiate Cortical and Subcortical Sub-Acute Ischemic Stroke Patients

Simulating the Evolution of Functional Brain Networks in Alzheimer’s Disease: Exploring Disease Dynamics from the Perspective of Global Activity

Homeostatic structural plasticity can account for topology changes following deafferentation and focal stroke

Contact Info

Product

Resources

About