Brain Connectivity Measures Improve Modeling of Functional Outcome After Acute Ischemic Stroke

Ktena, Sofia Ira; Schirmer, Markus D.; Etherton, Mark R.; Giese, Anne‐Katrin; Tuozzo, Carissa; Mills, Brittany; Rueckert, Daniel; Wu, Ona; Rost, Natalia S.

doi:10.1161/strokeaha.119.025738

Cited by 24 publications

(20 citation statements)

References 44 publications

(40 reference statements)

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“…The lesioned MCAO territory in the pig model was anatomically similar to human ischemic stroke patients and resulted in comparable functional outcomes, supporting the pig as a robust ischemic stroke model. It is important to note that other key elements such as functional and structural connectivity may also be critical components in predicting functional outcomes and should be further explored in future studies [62][63][64] . Nevertheless, using stroke location to improve outcome prediction is rapidly evolving in clinical practice.…”

Section: Discussionmentioning

confidence: 99%

Exploring the predictive value of lesion topology on motor function outcomes in a porcine ischemic stroke model

Scheulin

Jurgielewicz

Spellicy

et al. 2021

Sci Rep

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Harnessing the maximum diagnostic potential of magnetic resonance imaging (MRI) by including stroke lesion location in relation to specific structures that are associated with particular functions will likely increase the potential to predict functional deficit type, severity, and recovery in stroke patients. This exploratory study aims to identify key structures lesioned by a middle cerebral artery occlusion (MCAO) that impact stroke recovery and to strengthen the predictive capacity of neuroimaging techniques that characterize stroke outcomes in a translational porcine model. Clinically relevant MRI measures showed significant lesion volumes, midline shifts, and decreased white matter integrity post-MCAO. Using a pig brain atlas, damaged brain structures included the insular cortex, somatosensory cortices, temporal gyri, claustrum, and visual cortices, among others. MCAO resulted in severely impaired spatiotemporal gait parameters, decreased voluntary movement in open field testing, and higher modified Rankin Scale scores at acute timepoints. Pearson correlation analyses at acute timepoints between standard MRI metrics (e.g., lesion volume) and functional outcomes displayed moderate R values to functional gait outcomes. Moreover, Pearson correlation analyses showed higher R values between functional gait deficits and increased lesioning of structures associated with motor function, such as the putamen, globus pallidus, and primary somatosensory cortex. This correlation analysis approach helped identify neuroanatomical structures predictive of stroke outcomes and may lead to the translation of this topological analysis approach from preclinical stroke assessment to a clinical biomarker.

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Section: Discussionmentioning

confidence: 99%

Exploring the predictive value of lesion topology on motor function outcomes in a porcine ischemic stroke model

Scheulin

Jurgielewicz

Spellicy

et al. 2021

Sci Rep

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“…Combining hub-stratified subnetwork analysis with the above heat kernel framework is a complementary strategy to further our understanding of brain topology. The strategic importance of hubs for information transport makes them potentially vulnerable and thus sensitive to disease [28,26,18,9]. Whereas Fig.…”

Section: Discussionmentioning

confidence: 99%

“…This connectome approach recognises the distributed nature of higher order cognitive functions. Recent approaches have been to identify modules or subsets of regions that are most critical for efficient network function [27,26,18] and which exhibit specialisation for specific processes [11]. For one such approach, inter-connected brain regions of high functional or structural connectivity are considered to form a collection of core hubs, a subnetwork that is essential for efficient cognitive function.…”

Section: Introductionmentioning

confidence: 99%

Heat Kernels with Functional Connectomes Reveal Atypical Energy Transport in Peripheral Subnetworks in Autism

Schirmer

Chung

2019

Connectomics in NeuroImaging

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Autism is increasing in prevalence and is a neurodevelopmental disorder characterised by impairments in communication skills and social behaviour. Connectomes enable a systems-level representation of the brain with recent interests in understanding the distributed nature of higher order cognitive function using modules or subnetworks. By dividing the connectome according to a central component of the brain critical for its function (it's hub), we investigate network organisation in autism from hub through to peripheral subnetworks. We complement this analysis by extracting features of energy transport computed from heat kernels fitted with increasing time steps. This heat kernel framework is advantageous as it can capture the energy transported in all direct and indirect pathways between pair-wise regions over 'time', with features that have correspondence to small-world properties. We apply our framework to resting-state functional MRI connectomes from a large, publically available autism dataset, ABIDE. We show that energy propagating through the brain over time are different between subnetworks, and that heat kernel features significantly differ between autism and controls. Furthermore, the hub was functionally preserved and similar to controls, however, increasing statistical significance between groups was found in increasingly peripheral subnetworks. Our results support the increasing opinion of non-hub regions playing an important role in functional organisation. This work shows that analysing autism by subnetworks with the heat kernel reflects the atypical activations in peripheral regions as alterations in energy dispersion and may provide useful features towards understanding the distributed impact of this disorder on the functional connectome.

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“…However, limitations of this study included the small sample size (12 patients) and the inclusion of patients with ischaemic infarctions and intracranial haemorrhages. 23 More recently, other resting-state studies have reported that lower lesion load to strategic network areas was related to improved recovery of overall symptom burden 24 or of cognitive deficits after stroke. 25 Also these cohorts consisted of patients with rather mild to moderate symptom load with averaged NIHSS scores between 2 25 and 5.…”

Section: Introductionmentioning

confidence: 99%

Brain network topology early after stroke relates to recovery

Nemati

Backhaus

Feldheim

et al. 2022

Brain Communications

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Analyses of alterations of brain networks have gained an increasing interest in stroke rehabilitation research. Compared to functional networks derived from resting-state analyses, there is limited knowledge of how structural network topology might undergo changes after stroke and, more importantly, if structural network information obtained early after stroke could enhance recovery models to infer later outcome. The present work re-analysed cross-sectional structural imaging data, obtained within the first two weeks, of 45 acute stroke patients (22 females, 24 right-sided strokes, age 68±13 years). Whole-brain tractography was performed to reconstruct structural connectomes and graph-theoretical analyses were employed to quantify global network organisation with a focus on parameters of network integration and modular processing. Graph measures were compared between stroke patients and 34 healthy controls (15 females, aged 69±10 years) and they were integrated with four clinical scores of the late subacute stage, covering neurological symptom burden (National Institutes of Health Stroke Scale stroke scale), global disability (modified Rankin Scale), activity-related disability (Barthel-Index), and motor functions (Upper-Extremity Score of the Fugl-Meyer Assessment). The analyses were employed across the complete cohort and, based on clustering analysis, separately within subgroups stratified in mild to moderate (n=21) and severe (n=24) initial deficits. The main findings were (1) a significant reduction of network’s global efficiency, specifically in patients with severe deficits compared with controls (P=0.010), and (2) a significant negative correlation of network efficiency with the extent of persistent functional deficits at follow-up after three to six months (P≤0.032). Specifically, regression models revealed that this measure was capable to increase the explained variance in future deficits by 18 % for modified Rankin Scale, up to 24 % for National Institutes of Health Stroke Scale stroke scale), and 16 % for Barthel-Index when compared to models including the initial deficits and the lesion volume. Patients with mild to moderate deficits did not exhibit a similar impact of network efficiency on outcome inference. Clustering coefficient and modularity, measures of segregation and modular processing, did not exhibit comparable structure-outcome relationships, neither in severely nor in mildly affected patients. This study provides empirical evidence that structural network efficiency as a graph-theoretical marker of large-scale network topology, quantified early after stroke, relates to recovery. Notably, this contribution was only evident in severely but not mildly affected stroke patients. This suggests that the initial clinical deficit might shape the dependency of recovery on global network topology after stroke.

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Brain Connectivity Measures Improve Modeling of Functional Outcome After Acute Ischemic Stroke

Cited by 24 publications

References 44 publications

Exploring the predictive value of lesion topology on motor function outcomes in a porcine ischemic stroke model

Exploring the predictive value of lesion topology on motor function outcomes in a porcine ischemic stroke model

Heat Kernels with Functional Connectomes Reveal Atypical Energy Transport in Peripheral Subnetworks in Autism

Brain network topology early after stroke relates to recovery

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