Admission Brain Cortical Volume

Sagnier, Sharmila; Catheline, Gwénaëlle; Dilharreguy, Bixente; Munsch, Fanny; Poli, Mathilde; Debruxelles, Sabrina; Olindo, Stéphane; Renou, Pauline; Rouanet, François; Denis, Vincent; Tourdias, Thomas; Sibon, Igor

doi:10.1161/strokeaha.117.017646

Cited by 9 publications

(2 citation statements)

References 34 publications

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“…Recently, we developed a fully automated approach for quantifying WMH burden in low-resolution acute neuroimaging data, 21 which enables the quantification of this important biomarker at the bedside. Furthermore, brain volume at the time of admission has been demonstrated to be an important factor for determining functional and patient reported stroke outcome, 22–24 which outperforms estimates of brain atrophy as an independent factor in outcome models. 25…”

Section: Introductionmentioning

confidence: 99%

“…Recently, we developed a fully automated approach for quantifying WMH burden in lowresolution acute neuroimaging data, 21 which enables the quantification of this important biomarker at the bedside. Furthermore, brain volume at the time of admission has been demonstrated to be an important factor for determining functional and patient reported stroke outcome, [22][23][24] which outperforms estimates of brain atrophy as an independent factor in outcome models. 25 In this work, we sought to expand on our initial model of brain reserve, which relied on measures of intracranial volume and systolic blood pressure at the time of admission to characterize the maximum reserve of a patient and the acute vascular burden at time of stroke.…”

Section: Introductionmentioning

confidence: 99%

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Quantifying brain health in acute ischemic stroke through effective reserve

Schirmer,

Alhadid,

Regenhardt

et al. 2024

Preprint

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ObjectiveTo evaluate brain health through the use of expanded structural measures of reserve, that incorporate pre-existing pathology and cerebrovascular disease burden.BackgroundOutcome modeling at the time of stroke is a key challenge in patient care. The related concepts of brain health and reserve may help to understand the observed differences in patient outcomes. Effective reserve (eR) quantifies the brain’s capacity to compensate for negative effects, while accounting for pre-existing disease burden. Here, we extend the concept of eR by including measures of white matter hyperintensity (WMH) burden and compare the utility of brain volume and brain parenchymal fraction (BPF) to enhance its modeling capabilities.Design/MethodsAcute ischemic stroke patients from a single center between 2003-2011 with available neuroimaging data were included in this study. Modified Rankin Score (mRS) at 90 days post admission was used to assess functional outcome. Neuroimaging data were analyzed using dedicated deep-learning enabled pipelines to extract measures of WMH, brain, and intracranial volumes (ICV). BPF is given as the ratio of brain volume to ICV. eR is defined as a latent variable using structural equation modeling that includes age, WMH volume, and either BPF or brain volume. Models were compared using Bayes Information Criterion (BIC).Results476 patients were eligible for inclusion: median age 65.8 (interquartile range: 55.3-76.3) years, 65.3% male. There was an inverse association between eR and mRS in both brain volume and BPF models (path coefficients: -0.75 and -0.55, respectively; p<0.001). The model utilizing brain volume (BIC=4429.6) outperformed the model using BPF (BIC=4802.2).ConclusionsIn this work, we significantly extended the concept of eR and advanced its translational potential. The demonstrated association of higher eR and better post-stroke outcome signifies its potential as a descriptor of brain health, and a protective measure against acute ischemic injury.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Quantifying brain health in acute ischemic stroke through effective reserve

Schirmer,

Alhadid,

Regenhardt

et al. 2024

Preprint

View full text Add to dashboard Cite

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The new insights into human brain imaging after stroke

Sagnier

Sibon

2019

J of Neuroscience Research

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Over the last two decades, developments of human brain stroke imaging have raised several questions about the place of new MRI biomarkers in the acute management of stroke and the prediction of poststroke outcome. Recent studies have demonstrated the main role of perfusion‐weighted imaging in the identification of the best cerebral perfusion profile for a better response after reperfusion therapies in acute ischemic stroke. A major issue remains the early prediction of stroke outcome. While voxel‐based lesion‐symptom mapping emphasized the influence of stroke location, the analysis of the brain parenchyma underpinning the stroke lesion showed the relevance of prestroke cerebral status, including cortical atrophy, white matter integrity, or presence of chronic cortical cerebral microinfarcts. Moreover, besides the evaluation of the visually abnormal brain tissue, the analysis of normal‐appearing brain parenchyma using diffusion tensor imaging and magnetization transfer imaging or spectroscopy offered new biomarkers to improve the prediction of the prognosis and new targets to follow in therapeutic trials. The aim of this review was to depict the main new radiological biomarkers reported in the last two decades that will provide a more thorough prediction of functional, motor, and neuropsychological outcome following the stroke. These new developments in neuroimaging might be a cornerstone in the emerging personalized medicine for stroke patients.

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